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In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations. [ABSTRACT FROM AUTHOR]

Food Recommender Systems (RecSys) are innovative knowledge systems that inform consumers of food choices according to criteria, including nutritional content, health concerns, production method, carbon footprint or other social and ethical considerations. They raise important questions at the intersection of technology accuracy and today evolving consumers' knowledge seeking behaviours, which implies to unpack the epistemic value of food RecSys. This study investigates the drivers of the perceived value of food RSs consumption by proposing a model that establishes via PLS-SEM (n = 253) a positive relationship between the Yuka company's food RecSys' epistemic value and its perceived value. The model demonstrates that Yuka RecSys' epistemic value relies on the disciplinary drivers of compatibility, self-confidence, and consumer innovativeness, and the problematising drivers of memory and learning, which come from using the application. The perceived value of food RecSys is found to relate to RecSys epistemic value beyond the functional accuracy aspects of recommendation algorithms. Results highlight the importance of developing a refined understanding of epistemic value considering the consumption of RecSys. RecSys' developers, retailers, food manufacturers and policy makers must work on better mapping and adjusting information through consumers socialised RecSys' usage to shape the design of the next generation RecSys. [ABSTRACT FROM AUTHOR]

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines. [ABSTRACT FROM AUTHOR]

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring and care, illnesses, and sudden fish deaths. Another ongoing issue is automated fish species recommendation based on water quality. On the one hand, the effective monitoring of abrupt changes in water quality may minimize the daily operating costs and boost fish productivity, while an accurate automatic fish recommender may aid the farmer in selecting profitable fish species for farming. In this paper, we present AquaBot, an IoT-based system that can automatically collect, monitor, and evaluate the water quality and recommend appropriate fish to farm depending on the values of various water quality indicators. A mobile robot has been designed to collect parameter values such as the pH, temperature, and turbidity from all around the pond. To facilitate monitoring, we have developed web and mobile interfaces. For the analysis and recommendation of suitable fish based on water quality, we have trained and tested several ML algorithms, such as the proposed custom ensemble model, random forest (RF), support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), bagging, boosting, and stacking, on a real-time pond water dataset. The dataset has been preprocessed with feature scaling and dataset balancing. We have evaluated the algorithms based on several performance metrics. In our experiment, our proposed ensemble model has delivered the best result, with 94% accuracy, 94% precision, 94% recall, a 94% F1-score, 93% MCC, and the best AUC score for multi-class classification. Finally, we have deployed the best-performing model in a web interface to provide cultivators with recommendations for suitable fish farming. Our proposed system is projected to not only boost production and save money but also reduce the time and intensity of the producer's manual labor. [ABSTRACT FROM AUTHOR]

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Multimodal Naïve Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power. [ABSTRACT FROM AUTHOR]

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process. Author summary: The selection of a disease target, a biological entity with which potential drugs interact to treat diseases, is often the first step of a drug discovery project. Known to be extremely costly, drug discovery projects spend tremendous resources on failed drug instances, most of which result from inadequate choices of targets. To help address the costliness problem of drug discovery, we developed a machine learning-based framework that identifies biologically effective targets using a probabilistic knowledge graph built from both biological network data and literature evidence. Our method not only outperforms state-of-the-art baseline methods on the target prediction task, but can also identify targets with high biological relevance, as shown by the strong support of the literature for the predicted target candidates and wet lab experiments that validate the significance of the predicted target candidates for melanoma and colorectal cancer, These results suggest that our method can identify effective targets and therefore benefit drug discovery. [ABSTRACT FROM AUTHOR]

Social networks are widely used platforms for sharing various information and content, including text, images, and videos.The main challenge in social networking today is the verification of data credibility and the identification of genuine social media profiles. Cybercriminals take advantage of this by utilizing fake profiles to disseminate false information. However, current research mainly concentrates on spam filtering and analyzing malicious behavior separately, disregarding the interrelated nature of these issues. We propose a more desirable global, hybrid solution that encompasses both malicious profile detection and spam detection to mitigate the spread of spam effectively. This paper offers a deep learning-based method for detecting malicious profiles and spam tweets. For the profiles to interact with them as legitimate profiles, we first provide the detection of fake profiles using an automated honeypot. Next, we detect those who make interactions as malicious profiles, and finally, we collect their shared content to find spam tweets using a convolution neural network algorithm. We suggest using collaborative filtering and content filtering algorithms from recommender systems to define accounts similar to harmful profiles and spam similar to spam material picked up by convolution neural networks. We get a highly compelling and intriguing outcome with higher accuracy (99.23%) and lesser loss than typical learning algorithms. [ABSTRACT FROM AUTHOR]

The role of academic advising has been conducted by faculty-student advisors, who often have many students to advise quickly, making the process ineffective. The selection of the incorrect qualification increases the risk of dropping out, changing qualifications, or not finishing the qualification enrolled in the minimum time. This study harnesses a real-world dataset comprising student records across four engineering disciplines from the 2016 and 2017 academic years at a public South African university. The study examines the relative importance of features in models for predicting student performance and determining whether students are better suited for extended or mainstream programmes. The study employs a three-step methodology, encompassing data pre-processing, feature importance selection, and model training with evaluation, to predict student performance by addressing issues such as dataset imbalance, biases, and ethical considerations. By relying exclusively on high school performance data, predictions are based solely on students' abilities, fostering fairness and minimising biases in predictive tasks. The results show that removing demographic features like ethnicity or nationality reduces bias. The study's findings also highlight the significance of the following features: mathematics, physical sciences, and admission point scores when predicting student performance. The models are evaluated, demonstrating their ability to provide accurate predictions. The study's results highlight varying performance among models and their key contributions, underscoring the potential to transform academic advising and enhance student decision-making. These models can be incorporated into the academic advising recommender system, thereby improving the quality of academic guidance. [ABSTRACT FROM AUTHOR]

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. [ABSTRACT FROM AUTHOR]

AliAmvra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf (GP) to Artas' wider regions. In addition, this project aimed to implement an integrated plan of action to form a business identity with high added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this study is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmvra project, with a target goal of improving customer experience and product quality. An extensive analysis was conducted for this study by collecting data through surveys that took place in the gastronomes of the AliAmvra project. Our objective was to conduct two types of reviews, one focused in data analysis and the other on evaluating model-driven algorithms. Each review utilized a survey with an individual structure, with each one serving a different purpose. In addition, our model review focused its attention on developing a robust recommendation system with said data. The algorithms we evaluated were MLP (multi-layered perceptron), RBF (radial basis function), GenClass, NNC (neural network construction), and FC (feature construction), which were used for the implementation of the recommendation system. As our final verdict, we determined that FC (feature construction) performed best, presenting the lowest classification rate of 24.87%, whilst the algorithm that performed the worst on average was RBF (radial basis function). Our final objective was to showcase and expand the work put into the AliAmvra project through this analysis. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) technology has made remarkable strides in the music industry, profoundly impacting various aspects of our lives. This progress has extended to revolutionizing industries, streamlining processes, and enhancing personalization in music. AI's expanding capabilities have driven computer vision, natural language processing, and machine learning breakthroughs. As music enthusiasts increasingly turn to online platforms, the need for effective recommendation systems has become more pressing. The exponential growth of musical content poses a challenge in discovering music tailored to individual tastes. This paper proposed a personalized music recommendation system powered by deep learning, explicitly utilizing convolutional neural networks (CNNs) to address the challenges posed by expanding music libraries and diverse song resources. In today's era of ever-growing music databases and increasing content diversity, more than traditional retrieval methods are needed to efficiently connect users with their preferred track. Traditional music platforms rely solely on retrieval mechanisms, necessitating manual inputs such as artist names and titles to locate specific songs. However, this approach needs in-depth music data analysis and delivers personalized recommendations generated by various deep learning-based algorithms. This paper bridges this gap by harnessing deep learning algorithms for comprehensive music data analysis and personalized song suggestions. The proposed CNN-based recommendation system explores the domains of deep learning and content-based approaches, empowering users to discover their favorite tunes swiftly. This innovative mechanism leverages CNNs and deep learning algorithms to offer efficient, personalized, and data-driven music recommendations, transforming the user experience with their music collections. In experimental results, the proposed CNN model is compared with various deep learning algorithms, including DNN, RNN, and LSTM, as well as traditional algorithms like SVM, KNN, RF, and DT. Impressively, the CNN model achieved the highest accuracy at 95%, surpassing all other algorithms. [ABSTRACT FROM AUTHOR]

A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model's performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes. [ABSTRACT FROM AUTHOR]

Aim/Purpose This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user's characteristics to predict other centers of interest. Contribution This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendations for Researchers Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work. [ABSTRACT FROM AUTHOR]

This paper focuses on addressing the complex healthcare needs of patients struggling with discordant chronic comorbidities (DCCs). Managing these patients within the current healthcare system often proves to be a challenging process, characterized by evolving treatment needs necessitating multiple medical appointments and coordination among different clinical specialists. This makes it difficult for both patients and healthcare providers to set and prioritize medications and understand potential drug interactions. The primary motivation of this research is the need to reduce medication conflict and optimize medication regimens for individuals with DCCs. To achieve this, we allowed patients to specify their health conditions and primary and major treatment concerns, for example, costs of medication, interactions with current drugs, and weight gain. Utilizing data gathered from MTurk and Qualtrics, we gained insights into healthcare providers' strategies for making/customizing medication regimens. We constructed a dataset and subsequently deployed machine learning algorithms to predict optimal medication regimens for DCC patients with specific treatment concerns. Following the benchmarking different models, Random forest emerged as the top performer, achieving an accuracy of 0.93. This research contributes significantly to the enhancement of decision-making processes, empowers patients to take a more active role in their healthcare, and promotes more informed and productive discussions between patients and their care teams. [ABSTRACT FROM AUTHOR]

By combining blockchain technology (BT) and reinforcement learning (RL), the proposed work addresses the difficulties associated with vaccine recommendations. The need for individualized recommendations is obvious as vaccine schedules become more complicated and there are more vaccines available. The proposed work presents a novel approach that combines the adaptability of RL with the security, privacy, and transparency of BT. Layers for data processing, application, consensus, and smart contracts are included in the system architecture. It provides user‐managed secure access, individualized vaccine recommendations, and decentralized data storage. The system aims to improve public health outcomes by using smart contracts to automate procedures and RL to improve recommendations. Using 10‐fold cross‐validation on the Vaccine Adverse Event Reporting System (VAERS) dataset, the experimental study verifies the performance of the system. The study focuses on metrics like accuracy, sensitivity, specificity, and F‐measure when contrasting the proposed model with current solutions. The ability of proposed model to offer precise and well‐informed vaccine recommendations is demonstrated by its consistent outperformance of competitors in accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

A sentiment-based product recommendation system is a system that utilizes natural language processing to extract sentiment features from reviews and the machine learning algorithms namely logistic regression, random forest classifier, XGBoost classifier and CatBoost classifier are applied to classify sentiments and these algorithms performance is evaluated based on the performance metrics such as accuracy, recall, precision and F1 score. Memory based collaborative filtering models such as user based and item-based recommendation models are developed and assessed using benchmark data, its performance is evaluated based on the evaluation metric Root mean square error (RMSE) and then the recommendations are fine tuned. While real-time data is obtained through web scraping from the Mamaearth website. Sentiment-based recommendations are implemented on real-time data, and their performance is evaluated using the same key performance metrics and the recommendations are fine-tuned. Finally, a Streamlit web app is created and deployed using Ngrok to enhance the accessibility and utility of the recommendation system. [ABSTRACT FROM AUTHOR]

Farming is the primary lifestyle in numerous nations, particularly India. Assuming they have never worked, youthful ranchers may not realize which yields to plant and which ones to pick. We're taking care of on this problem, which should be fixed. In the event that you can foresee the right yield and result, you can go with better choices, set aside cash, and hold the gamble of cost changes under wraps. Rather than our framework, which utilizes classification and relapse to sort out crop assortment ideas and result appraises, the ongoing framework isn't being utilized. ML strategies ought to be utilized in the cultivation business to foresee the quantity of harvests that will become in view of a bunch of information. The directed ML technique is utilized to take a gander at a dataset and pull information from various sources, like variable unmistakable confirmation, univariate examination, bivariate and multivariate investigation, missing worth frameworks, and so forth. To figure out which program was supposed to give the best return, ML recipes were investigated. While seeing entropy precision, accuracy, recall, F1 Score, sensitivity, specificity, and entropy, the outcomes show that the recommended ML strategy is the most reliable. [ABSTRACT FROM AUTHOR]

Drug Supply Chain Management (DSCM) can be one of the most affected streams in healthcare due to pandemics. The delivery of medicine to patients through DSCM is a complex process. Also, DSCM has several challenges, including counterfeiting, fraud, and the availability of medicine. Therefore, there is a need for security and intelligence strategies to remove pharmaceutical fraud, which remains a significant challenge since ensuring fair and secure access to medicine, services, and assistance is essential in Cyber-Physical Systems (CPS)-based DSCM. The existing CPS-based DSCM systems do, however, have some limitations in security, intelligence, planning, scheduling, quality, and logistics. This paper proposes a secure drug supply chain management framework that can acheive more security and intelligence via machine learning models. The proposed framework utilizes Bidirectional Encoder Representations from Transformers (BERT)-based and machine learning-based attack detection modules to provide more intelligence and security in blockchain-based DSCM. Evaluation results show that BERT-based recommender systems ideally suggest appropriate alternative drugs that are close to 99 % similar to the prescribed medication based on public datasets. Moreover, attack detection in the proposed framework provides significant accuracy, precision, recall, and F-measure results in threat detection (phishing, scamming, and abnormal transactions) in the blockchain layer. [ABSTRACT FROM AUTHOR]

Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset. [ABSTRACT FROM AUTHOR]

Recommendation system and classification machine learning techniques have emerged as a major area of investigation into the issue of information overload. Software tools called "recommender systems" aim to help users make informed choices about services. This paper developed a low-dimensional embedding in which a limited number of features can be used to represent the data object. For the purpose of acquiring the low rank latent factors, matrix factorization techniques have received a lot of attention. The proposed model is stated as the Collaborative Filtering Machine Learning (CFML) for the multi-label classification are focused. In most extreme edge lattice factorization plan of cooperative sifting, appraisals framework with different discrete qualities is treated by uncommonly st retching out pivot misfortune capability to suit numerous levels. The comparative analysis is performed for the two-class classifier into a single multiclass classifier. Alternately, multiple two-class classifiers can be arranged in a hierarchical fashion to create a multi-class classifier. To deal with the ordinal rating matrix's completion. The performance of the CFML model is effective for the recommendation system design. Through automatic feature extraction and pattern recognition, deep learning models excel at capturing intricate relationships and latent factors that underlie user preferences. The paper discusses the multifaceted considerations that these systems can incorporate, such as genre, cast, director, viewer ratings, and individual viewing history. Simulation analysis examine the impact of deep learning-based movie recommenders on streaming platforms and the entertainment industry, demonstrating their ability to not only suggest movies but to curate a personalized journey through the world of cinema. [ABSTRACT FROM AUTHOR]

Given a user dataset U and an object dataset I , a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I . The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems, and many more. In the real world, datasets frequently update dynamically as objects are added or removed. In this paper, we propose novel methods of continuous kNN join over dynamic high-dimensional data. We firstly propose the HDR + Tree, which supports more efficient insertion, deletion, and batch update. Further observed that the existing methods rely on globally correlated datasets for effective dimensionality reduction, we then propose the HDR Forest. It clusters the dataset and constructs multiple HDR Trees to capture local correlations among the data. As a result, our HDR Forest is able to process non-globally correlated datasets efficiently. Two novel optimisations are applied to the proposed HDR Forest, including the precomputation of the PCA states of data items and pruning-based kNN recomputation during item deletion. For the completeness of the work, we also present the proof of computing distances in reduced dimensions of PCA in HDR Tree. Extensive experiments on real-world datasets show that the proposed methods and optimisations outperform the baseline algorithms of naive RkNN join and HDR Tree. [ABSTRACT FROM AUTHOR]

Recommender systems commonly encounter with the problems of data sparsity and cold start. Recently, social recommendation has emerged with the rapid expansion of social platforms, offering an opportunity to alleviate such two obstacles. Nevertheless, there are still two key limitations in existing studies. From the perspective of model design, previous social recommenders only consider the influence of a user's direct friends or uniformly treat the influences from different friends. From the perspective of model learning, most of them apply a sampling‐based optimization strategy, which requires high‐quality positive and negative samples. In light of the aforementioned limitations, we propose a new probabilistic method, named Graph Attentive Matrix Factorization (GAMF). Our method not only explicitly captures high‐order social relationships, but also adopts an attention mechanism to automatically pick up different influences between friends. Moreover, we develop an efficient optimization algorithm to learn model parameters in a non‐sampling manner. Extensive experiments on four large‐scale datasets show the superiority of GAMF over state‐of‐the‐art recommenders, especially under the cold start scenario. [ABSTRACT FROM AUTHOR]

A sentiment-based product recommendation system is a system that utilizes natural language processing to extract sentiment features from reviews and the machine learning algorithms namely logistic regression, random forest classifier, XGBoost classifier and CatBoost classifier are applied to classify sentiments and these algorithms performance is evaluated based on the performance metrics such as accuracy, recall, precision and F1 score. Memory based collaborative filtering models such as user based and item-based recommendation models are developed and assessed using benchmark data, its performance is evaluated based on the evaluation metric Root mean square error (RMSE) and then the recommendations are fine tuned. While real-time data is obtained through web scraping from the Mamaearth website. Sentiment-based recommendations are implemented on real-time data, and their performance is evaluated using the same key performance metrics and the recommendations are fine-tuned. Finally, a Streamlit web app is created and deployed using Ngrok to enhance the accessibility and utility of the recommendation system. [ABSTRACT FROM AUTHOR]

Motivation Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training. Results Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data. Availability and implementation Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro. [ABSTRACT FROM AUTHOR]

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond. [ABSTRACT FROM AUTHOR]

In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user's or patient's illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user's extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer’s local disk for handling larger dataset sizes in the future. [ABSTRACT FROM AUTHOR]

Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models. [ABSTRACT FROM AUTHOR]

Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches. [ABSTRACT FROM AUTHOR]

Millions of individuals use Twitter, one of the most prominent social media sites, to exchange information, broadcast tweets, and follow other users. Twitter being an open application programming interface is vulnerable to attacks from fake accounts. Fake accounts are primarily used for advertising and marketing, defamation of an individual, consumer data acquisition, increasing fake blog or website traffic, sharing wrong information, online fraud, and control. Fake accounts are disruptive to both users and service providers, thus it's critical to recognize and filter out such information on social media. This paper presents a technique for detecting fake Twitter accounts using the feature set mapped to Twitter's rules and policies to flag suspicious accounts. To choose the apt subset of features from the original feature space, feature selection techniques such as information gain and correlation were used. Users have been classified using a Logistic Regression classifier with the detection accuracy of 93.5 percent after being trained on the data of about 1,00,000 Twitter users. Furthermore, an algorithm for classifying suspicious users as Fake profiles has been presented. The results of the experiments show that the suggested model outperforms competitive state-of-the-art research in the majority of circumstances. [ABSTRACT FROM AUTHOR]

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world. [ABSTRACT FROM AUTHOR]

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents' consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations. [ABSTRACT FROM AUTHOR]

Chronic diseases are increasingly major threats to older persons, seriously affecting their physical health and well-being. Hospitals have accumulated a wealth of health-related data, including patients' test reports, treatment histories, and diagnostic records, to better understand patients' health, safety, and disease progression. Extracting relevant information from this data enables physicians to provide personalized patient-treatment recommendations. While collaborative filtering techniques and classical algorithms such as naive Bayes, logistic regression, and decision trees have had notable success in health-recommendation systems, most current systems primarily inform users of their likely preferences without providing explanations. This paper proposes an approach of deep learning with a local interpretable model–agnostic explanations (LIME)-based interpretable recommendation system to solve this problem. Specifically, we apply the proposed approach to two chronic diseases common in older adults: heart disease and diabetes. After data preprocessing, we use six deep-learning algorithms to form interpretations. In the heart-disease data set, the actual model recommendation of multi-layer perceptron and gradient-boosting algorithm differs from the local model's recommendation of LIME, which can be used as its approximate prediction. From the feature importance of these two algorithms, it can be seen that the CholCheck, GenHith, and HighBP features are the most important for predicting heart disease. In the diabetes data set, the actual model predictions of the multi-layer perceptron and logistic-regression algorithm were little different from the local model's prediction of LIME, which can be used as its approximate recommendation. Moreover, from the feature importance of the two algorithms, it can be seen that the three features of glucose, BMI, and age were the most important for predicting heart disease. Next, LIME is used to determine the importance of each feature that affected the results of the calculated model. Subsequently, we present the contribution coefficients of these features to the final recommendation. By analyzing the impact of different patient characteristics on the recommendations, our proposed system elucidates the underlying reasons behind these recommendations and enhances patient trust. This approach has important implications for medical recommendation systems and encourages informed decision-making in healthcare. [ABSTRACT FROM AUTHOR]

Motivation CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs–disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA–disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA–disease interactions. Results In this study, a multilayer attention neural graph-based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as the initial features of circRNAs and diseases. Then, by constructing a central network of different diseases and circRNAs, a multilayer cooperative attention-based message propagation is performed on the central network to obtain the high-order features of circRNAs and diseases. A neural network-based collaborative filtering is constructed to predict the unknown circRNA–disease associations and update the model parameters. Experiments on the benchmark datasets demonstrate that MLNGCF outperforms state-of-the-art methods, and the prediction results are supported by the literature in the case studies. Availability and implementation The source codes and benchmark datasets of MLNGCF are available at https://github.com/ABard0/MLNGCF. [ABSTRACT FROM AUTHOR]

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases. [ABSTRACT FROM AUTHOR]

Discovery of new compounds from wide chemical space is attractive for materials researchers. However, theoretical prediction and validation experiments have not been systematically integrated. Here, we demonstrate that a new combined approach is powerful in significantly accelerating the discovery rate of new compounds, which should be useful for exploration of a wide chemical space in general. A recommender system for chemically relevant composition is constructed by machine learning of Inorganic Crystal Structure Database using chemical compositional descriptors. Synthesis and identification experiments are made at the chemical compositions with high recommendation scores by the single-particle diagnosis method. Two new compounds, La4Si3AlN9 and La26Si41N80O, and two new variants (isomorphic substitutions) of known compounds, La7Si6N15 and La4Si5N10O, are successfully discovered. Finally, density functional theory calculations are conducted for La4Si3AlN9 to confirm the energetic and dynamical stability and to reveal its atomic arrangement. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been universally adopted for automated decisions in a variety of fields, including recognition and classification applications, recommendation systems, natural language processing, and so on. However, in light of high expenses on training data and computing resources, recent years have witnessed a rapid increase in outsourced ML training, either partially or completely, which provides vulnerabilities for adversaries to exploit. A prime threat in training phase is called poisoning attack, where adversaries strive to subvert the behavior of machine learning systems by poisoning training data or other means of interference. Although a growing number of relevant studies have been proposed, the research among poisoning attack is still overly scattered, with each paper focusing on a particular task in a specific domain. In this survey, we summarize and categorize existing attack methods and corresponding defenses, as well as demonstrate compelling application scenarios, thus providing a unified framework to analyze poisoning attacks. Besides, we also discuss the main limitations of current works, along with the corresponding future directions to facilitate further researches. Our ultimate motivation is to provide a comprehensive and self-contained survey of this growing field of research and lay the foundation for a more standardized approach to reproducible studies. [ABSTRACT FROM AUTHOR]

Online shopping is a form of electronic commerce that enables customers to purchase products directly from the seller over the internet using a web browser or a mobile application. With the growing number of research papers on Deep Learning (DL)-based recommendations, DL models have proven to be effective in Recommender systems. Hence in this paper, relevant product accessories recommendation is proposed using deep learning and histogram features. The proposed recommendation describes the novel way of content-based filtering which divides the work into two phases. In phase 1, the implemented approach determines the color and category of the product. In phase 2, the recommendation system retrieves the matching accessories for the chosen product. For phase 1, Decision Tree, Linear Regression, Logistic Regression, Naïve Bayes, Long Short-Term Memory (LSTM), and a novel method of Stack dense Recurrent Neural Network (RNN) along with the color histogram is applied to identify the category and color of the product. It is justified that the proposed technique of stack dense RNN achieves 98% accuracy. In phase 2, from the Machine Learning (ML) perspective, Random Forest (RF) is employed for the recommendation. The RF model finds the matching accessories for the chosen product and retrieves the top relevant items. [ABSTRACT FROM AUTHOR]

Personalized points-of-interest (POI) recommendation is very important for improving the service quality of location-based social network applications. It has become one of the most popular research directions in the industry and academia. However, the realization of high-quality personalized POI recommendation faces three major challenges: (i) the interest drift issue caused by the spatiotemporal dynamics of user check-in behavior, (ii) how to integrate as much heterogeneous information as possible to alleviate data sparseness and cold start issues, and (iii) how to use implicit feedback to model complex high-order nonlinear user-POI interactions. To jointly address all these challenges, we propose the TGVx recommendation framework and establish a dynamic personalized POI deep recommendation model, where T and G respectively represent time and geographic factors, V represents out-of-town visitors, and x represents time slot number. TGVx is composed of x parallel TGV models where the TG module mines high-order nonlinear user-POI interaction relationships and integrates multisource heterogeneous information, and the V module transfers the check-in records of out-of-town visitors in hometowns and generates pseudo check-in records in the target city. Technically, we design a new unsupervised deep learning network T-SemiDAE for the TG module. We built a POI-word heterogeneous network for the V module and used graph embedding technology to match the most similar POIs across cities and transfer check-in records. The experimental results on the actual datasets show that the TGVx model is always better than other advanced models in terms of accuracy and diversity for local and out-of-town recommendation scenarios. Compared with the best baseline model semi-deep auto-encoder with a conditional layer the average improvement rates of accuracy and diversity of TGVx are 17.1% to 58.6% and 2.25% to 28.86%, respectively. In theory, our research effectively uses data science and analysis methods to design a recommender system. In practice, our research is motivated by practical problems, and the research results have high practical promotion value. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71831003, 72293563, and 72172025], the Dalian Science and Technology Talent Innovation Support Policy Project [The High-level Talent Innovation Team Project 2022RG17], and the Liaoning Provincial Department of Education [Basic Scientific Research Project LJKMZ20221582]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1286) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0010) at (http://dx.doi.org/10.5281/zenodo.7407123). [ABSTRACT FROM AUTHOR]

Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received close attention from researchers in all sectors of society. In this paper, we first propose a feature engineering method based on label distribution learning, which analyzes historical behavior is analyzed and constructs, whereby feature vectors are constructed for users and products via label distribution learning. Then, a recommendation algorithm based on value distribution reinforcement learning is proposed. We first designed the stochastic process of the recommendation process, described the user's state in the interaction process (by including the information on their explicit state and implicit state), and dynamically generated product recommendations through user feedback. Next, by studying hybrid recommendation strategies, we combined the user's dynamic and static information to fully utilize their information and achieve high-quality recommendation algorithms. Finally, the algorithm was designed and validated, and various relevant baseline models were compared to demonstrate the effectiveness of the algorithm in this study. With this study, we actually tested the remarkable advantages of relevant design models based on nonlinear expectations compared to other homogeneous individual models. The use of recommendation systems with nonlinear expectations has considerably increased the accuracy, data utilization, robustness, model convergence speed, and stability of the systems. In this study, we incorporated the idea of nonlinear expectations into the design and implementation process of recommendation systems. The main practical value of the improved recommendation model is that its performance is more accurate than that of other recommendation models at the same level of computing power level. Moreover, due to the higher amount of information that the enhanced model contains, it provides theoretical support and the basis for an algorithm that can be used to achieve high-quality recommendation services, and it has many application prospects. [ABSTRACT FROM AUTHOR]

People often struggle to do what they ideally want because of a conflict between their actual and ideal preferences. ​​​By focusing on maximizing engagement, recommendation algorithms appear to be exacerbating this struggle. However, this need not be the case. Here we show that tailoring recommendation algorithms to ideal (vs. actual) preferences would provide meaningful benefits to both users and companies. To examine this, we built algorithmic recommendation systems that generated real-time, personalized recommendations tailored to either a person's actual or ideal preferences. Then, in a high-powered, pre-registered experiment (n = 6488), we measured the effects of these recommendation algorithms. We found that targeting ideal rather than actual preferences resulted in somewhat fewer clicks, but it also increased the extent to which people felt better off and that their time was well spent. Moreover, of note to companies, targeting ideal preferences increased users' willingness to pay for the service, the extent to which they felt the company had their best interest at heart, and their likelihood of using the service again. Our results suggest that users and companies would be better off if recommendation algorithms learned what each person was striving for and nudged individuals toward their own unique ideals. [ABSTRACT FROM AUTHOR]

Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. [ABSTRACT FROM AUTHOR]

In today's online market, recommendation systems have become universal and are an aspect of any online shopping portal. The traditional approach uses the subscriber's historical knowledge, and this technique is not adequate for resolving problems with a cold start. These issues include recommendations for non-registered users or newly added customers and new items added. Session-based recommendations based on recurrent neural networks are gaining popularity for product recommendations. This is due to recurrent neural networks' ability to record sequential feature data more effectively throughout the current session, which results in more similarity between consumer behaviour sequences. Nevertheless, most state-of-the-art recurring neural networking systems completely ignore the long-term details of multiple sessions and concentrate solely on short-term communication in a single session. This paper presents a hybrid time-centric prediction model to address research issues that learn the customers' short and long-term behaviours. Experiments on the recsys challenge data set are carried out to assess the efficiency of the hybrid time-centric prediction models over the existing hybrid models in terms of HitRate and Mean-Reciprocal Rate. [ABSTRACT FROM AUTHOR]

Recent research works have shown the robustness towards the recommendation system for athletics using an AI automated system that enhances the longevity of the system. With this model, the automated recommendation helps to improve the quality of athletes during the process of training or other processes. Moreover, domain experts only can understand the rationale of the recommender system where the analyzed data is stored in the cloud system. This research proposes a machine learning–based solution for an athletic dataset that automatically predicts the state of the individual with features like age, gender, calories, temperature, pressure, heart rate, pulse rate, sugar level, respiratory conditions, and state of the body. This research concentrates on modeling a framework for implementing the machine learning approaches with an optimization problem. Here, a novel extreme multi-gradient evolutionary computation (EMGEC) with improved grey wolf optimization (IGWO) is proposed to achieve exploration and exploitation during the selection of features. The dataset collected from the athletes during the marathon (running) is collected from online resources and the feature subsets are extracted from the dataset. The features of these data are analyzed and encoded before placing it over the cloud environment. The performance of the proposed machine learning approach is compared with other approaches and provides better prediction accuracy, precision, recall, and F-measure respectively. The accuracy of the anticipated model is 83.13%, precision is 91.1%, and recall is 91.3% which is substantially higher than of other approaches. The proposed model shows a better trade-off in contrast to prevailing approaches like SVM, RF, k-NN, and logistic regression. [ABSTRACT FROM AUTHOR]

In the practical application of recommendation systems, item popularity bias can be amplified by feedback loops, machine learning training models, and some external factors. It results in a phenomenon where a large number of long-tail items do not get a fair chance to be recommended. To address the fairness problem caused by the feedback loop amplifying the popularity bias, this paper conducted the first fairness analysis and enhancement study by means of a stochastic model checking method. It modeled the traditional recommendation system framework based on popularity bias and feedback loops as a DTMC and verified the fairness properties. The experiment revealed that as the number of feedback loop rounds increased, the Matthew effect intensified and fairness significantly diminished. This paper presented a fairness-enhanced recommendation system framework (FERSF) guided by stochastic model checking. It added a dynamic fairness threshold detection process to the feedback loop of the traditional framework to monitor the fairness. Also, it made a fairness-enhanced adjustment of the feedback influence factor to mitigate the impact of popularity bias on the system. The experimental analysis shows that the fairness of FERSF is significantly improved compared to the traditional recommendation system. Compared with the methods based on utility functions for fairness improvement, FERSF fundamentally inhibits the amplification of popularity bias due to the dynamic nature of the combined feedback loop. Compared with other algorithm-specific fairness improvements, FERSF is highly compatible because it is modeled based on the recommendation system framework. [ABSTRACT FROM AUTHOR]

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human–computer interaction, psychology, and social sciences. [ABSTRACT FROM AUTHOR]

Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user's perspective, adopting a cloud service's trustworthiness becomes crucial. Trust is a measure of how well users' expectations about a service's capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC's average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies. [ABSTRACT FROM AUTHOR]

The performance of recommendation engines is challenged by the cold-start issues. Classical recommendation techniques have limited capability to address this problem because the underlying machine learning models are data-thirsty. Conversely, the recent success of meta-learning attracted a lot of attention due to few-shot learning capabilities which can be employed to accommodate new tasks even when there are few data samples for training. Most of the meta-learning recommendation models adopt model-agnostic meta-learning to initialize parameters that may lead to stuck into local optima instead of global optima for some users. To leverage the learning process, we propose a Contextually Augmented Meta-Learning recommender system (CAML). The proposed method augments the contextual features into a meta-learning model which considerably improves the tasks adaptation capability. We constructed a Data Augmentation Unit (DAU) that used a hybrid similarity to augment data samples of similar neighbors. The augmented samples are then forwarded to a Meta-Learner (MetaL) to learn user preferences and generate relevant recommendations. Additionally, we highlighted that the proposed method is generalized and can be adapted for various meta-learning-based recommendation models. We validate our proposal on three benchmark datasets for both ranking and rating prediction perspectives. The experimental outcomes prove the significance of our proposal over the state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]

The e-learning is necessary in this fast internet world, especially during this pandemic situation, to continue education without any interruption and it is used reduce the educational cost significantly when reduces the energy loss. Generally, machine learning and deep learning algorithms are used to identify patterns that facilitate learning and help learners understand concepts easily. Many content recommendation systems are available for assisting learners as e-learning applications by providing the required study materials. Despite the fact that existing recommendation systems struggle to provide precise content to e-learners due to the availability of a massive volume of data on the internet and other repositories. For this purpose, we propose a new content recommendation system for recommending suitable content to learners according to their interests and learning capabilities. The proposed content recommendation system employs a newly proposed semantic-aware hybrid feature optimizer that incorporates new optimization algorithms such as the Enhanced Personalized Best Cuckoo Search Algorithm (EpBestCSA) and the Enhanced Harris Hawks Optimization Algorithm (EHHOA) for selecting suitable features that aid in improving prediction accuracy, as well as a newly proposed Deep Semantic Structure Model (DSSM) that incorporates Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). According to the experimental results, the proposed model outperforms other recommendation systems in terms of precision, recall, f-measure, and prediction accuracy. The ten-fold cross validation is done to test the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]