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Semantic similarity has been gaining traction in the field of natural language processing. It is a measure of how similar two pieces of text are in terms of their meaning. It can be used to improve search engine results. We propose a deep learning-based approach to build a semantic similarity search engine for movies based on a movie summary. Filmmakers can gain insight into audience preferences and trends, allowing them to create more engaging and successful films. The dataset used in this study was gathered from internet movie database (IMDb), it includes movie summaries along with their corresponding name movies. The test dataset was generated using ChatGPT to be very close to human input. The universal sentence encoder (USE) model presented promising results in semantic similarity, the model results show that for the top 5 similar movies, the model returned 176 out of 300 movies (58.6%). For the top 10 similar movies, the model returned 211 out of 300 movies (70.3%). Additionally, for the top 15 similar movies, the model returned 229 out of 300 movies (76.3%). And, for the top 20 similar movies, the model returned 249 of 300 movies (83%). This method can be applied to movie recommendation systems or to organize films in a collection automatically. [ABSTRACT FROM AUTHOR]

The prevalence of Recommender Systems (RS) has surged due to the widespread growth of the Internet and its related technologies. The efficiency of the RS is dependent on the quality of the underlying datasets used for training. Sparse datasets present a challenge for training such systems and negatively impacts their efficiency. This research paper presents novel synergistic approaches that combine Fuzzy C-Means (FCM), Restricted Boltzmann Machines (RBM), and Extreme Learning Machines (ELM), to enhance Collaborative Filtering (CF) in RS. The study introduces two clustering-based approaches, FCM-ELM and FCM-RBM-ELM, to address the issue of non-optimal hidden biases and input weights in ELM caused by random assignment in order to overcome the data sparsity effect in RS. The efficiency of proposed approaches against different clustering methods has been evaluated using two benchmark datasets and four metrics. The empirical results indicate that the proposed methods can generate better recommendation results compared to other traditional techniques. [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]

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience. [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]

Currently, there is a significant amount of research being conducted in the field of artificial intelligence to improve the explainability and interpretability of deep learning models. It is found that if end-users understand the reason for the production of some output, it is easier to trust the system. Recommender systems are one example of systems that great efforts have been conducted to make their output more explainable. One method for producing a more explainable output is using counterfactual reasoning, which involves altering minimal features to generate a counterfactual item that results in changing the output of the system. This process allows the identification of input features that have a significant impact on the desired output, leading to effective explanations. In this paper, we present a method for generating counterfactual explanations for both tabular and textual features. We evaluated the performance of our proposed method on three real-world datasets and demonstrated a +5% improvement on finding effective features (based on model-based measures) compared to the baseline method. [ABSTRACT FROM AUTHOR]

Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aimof this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection and classification, the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities. This system is based on the calculation of calories burned by each user during physical activities. In this way, conclusions can be drawn about a person's physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts. To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory (LSTM), stacked LSTM, and bidirectional LSTM. Since human activity data contains both spatial and temporal information, we proposed, in this paper, to use of an architecture allowing the extraction of the two types of information simultaneously. While Convolutional Neural Networks (CNN) has an architecture designed for spatial information, our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data. The results obtained achieved an accuracy of 96%. On the other side, the data learned by these algorithms is prone to error and uncertainty. To overcome this constraint and improve performance (96%), we proposed to use the fusion mechanisms. The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making. The Voting and Dempster-Shafer (DS) approaches are employed. The results showed that fused classifiers based on DS theory outperformed individual classifiers (96%) with the highest accuracy level of 98%. Also, the findings disclosed that participants engaging in physical activities are healthy, showcasing a disparity in the distribution of physical activities between men and women. [ABSTRACT FROM AUTHOR]

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers. [ABSTRACT FROM AUTHOR]

In recent times, recommender systems have acquired significant popularity as a solution to the issue of information overload. These systems offer personalised recommendations to users. The superiority of multi-criteria recommender systems over their single-criterion counterparts has been demonstrated, as the former are able to provide more precise recommendations by taking into account multiple dimensions of user preferences when rating items. The prevalent recommendation technique, matrix factorization of collaborative filtering, is hindered by the data sparsity problem of the user-item matrix. On the other hand, it is noteworthy that deep learning techniques have demonstrated significant potential in various research domains, including but not limited to image processing, pattern recognition, computer vision, and natural language processing. In recent times, there has been a surge in the utilisation of deep learning techniques in recommender systems, yielding promising outcomes. This study presents a novel approach to multi-criteria recommender systems through the utilisation of deep learning algorithms to mitigate the data sparsity issue. Specifically, deep autoencoders are utilised to uncover complex, non-linear, and latent relationships between users' multi-criteria preferences followed by matrix factorization technique, ultimately leading to more precise recommendations. The proposed model is evaluated by conducting the experiments on the multi-criteria dataset of Yahoo! Movies. According to the outcomes, the proposed approach outperforms the state of the art recommendation methods by generating more accurate and personalized recommendations. Also, it reduces the data sparsity up to 11% from the multi-criteria dataset. [ABSTRACT FROM AUTHOR]

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

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]

The proliferation of uncategorized information on the Internet has intensified the need for effective recommender systems. Recommender systems have evolved from content-based filtering to collaborative filtering and, most recently, to deep learning-based and hybrid models. However, they often face challenges such as high computational costs, reduced reliability, and the Cold Start problem. We introduce a persona-based user modeling approach for real-time movie recommendations. Our system employs Non-negative Matrix Factorization (NMF) and Deep Learning algorithms to manage complex and sparse data types and to mitigate the Cold Start issue. Experimental results, based on criteria involving 50 topics and 35 personas, indicate a significant performance gain. Specifically, with 500 users, the precision@K for NMF was 86.01%, and for the Deep Neural Network (DNN), it was 92.67%. Tested with 900 users, the precision@K for NMF increased to 97.04%, and for DNN, it was 95.55%. These results represent an approximate 10% and 5% improvement in performance, respectively. The system not only delivers fast and accurate recommendations but also reduces computational overhead by updating the model only when user personas change. The generated user personas can be adapted for other recommendation services or large-scale data mining. [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]

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]

The recommendation system (RS) on the strength of Graph Neural Networks (GNN) perceives a user-item interaction graph after collecting all items the user has interacted with. Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession. However, user preferences are dynamic. With the passage of time and some trend guidance, users may generate some short-term preferences, which are more likely to lead to user-item interactions. A GNN recommendation based on long- and short-term preference (LSGNN) is proposed to address the above problems. LSGNN consists of four modules, using a GNN combined with the attention mechanism to extract long-term preference features, using Bidirectional Encoder Representation from Transformers (BERT) and the attention mechanism combined with Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract short-term preference features, using Convolutional Neural Network (CNN) combined with the attention mechanism to add title and description representations of items, finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations. In experiments conducted on five publicly available datasets from Amazon, LSGNN is superior to state-of-the-art personalized recommendation techniques. [ABSTRACT FROM AUTHOR]

Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers' travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios. [ABSTRACT FROM AUTHOR]

Recommendation systems serve as a pivotal solution to address the increasing issue of information overload. While traditional recommendation algorithms have been grounded primarily on user-item interactions, the significance of a user's contextual information influencing decision-making has often been overlooked. Such neglect becomes more evident in the realm of systems integrating contextual mechanisms, which encounter pronounced data sparsity challenges. Existing studies in contextual recommendation systems tend to treat all contextual features uniformly as influencers of user decisions. Yet, a prevalent dilemma is the frequent absence of contextual data, leading to potential misallocations of contextual features. To mitigate these challenges, a novel deep learning-based recommendation system, termed the CAW-NeuMF Model, has been designed. Accompanying this model, a Context-aware Weighted high-order Tensor Factorization algorithm (CAWTF) has been introduced. This algorithm facilitates the calculation of correlations between user ratings in varied contexts, relying on the said context. Additionally, it ascertains the weight of context features grounded on the user ratings correlation. Such a process aids in isolating the most influential contextual features, thereby amplifying the efficiency of personalized recommendations. Empirical evaluations using the LDOS CoMoDa dataset revealed that the proposed model substantially enhances prediction score accuracy. Comparative analyses against alternative recommendation models further affirmed the superior efficacy of the introduced approach. [ABSTRACT FROM AUTHOR]

Background: Telemedicine offers opportunity for robust diagnoses recommendations to support healthcare providers intra-consultation in a way that does not limit providers ability to explore diagnostic codes and make the most appropriate selection for each consultation. Objective: The objective of this work was to develop a recommendation system for ICD-10 coding using multiclass sequence classification and deep learning. The recommendations are intended to support telemedicine clinicians in making timely and appropriate diagnosis selections. The recommendations allow clinicians to find and select the best diagnosis code much quicker and without leaving the telemedicine platform to search codes and code descriptions. Methods: We developed an LSTM model for multi-class text sequence classification to make diagnosis recommendations. The LSTM recommender used text-based symptoms, complaints, and consultation request reasons as model inputs. Data were extracted from a live telemedicine platform which spans general medicine, dermatology, and mental health clinical specialties. A popularity-based model was used for baseline comparison. Results: Using over 2.8 MM telemedicine consultations during 2021 and 2022, our LSTM recommender average accuracy was 31.7%. LSTM recommender average coverage in the top 20 recommended diagnoses was 85.8% with an average personalization score of 0.87. Conclusions: LSTM multi-class sequence classification recommends diagnoses specific to individual consultations, is retrainable on regular intervals, and could improve diagnoses recommendations such that providers require less time and resources searching for diagnosis codes. In addition, the LSTM recommender is robust enough to make recommendations across clinical specialties such as general medicine, dermatology, and mental health.

With the advancement of Internet, recommendation systems have become an indispensable component for every e-commerce platform, playing an increasingly pivotal role in product recommendations. However, due to the recommendation mechanisms of these systems, numerous new attack patterns have emerged. A novel attack pattern targeting e-commerce recommendation systems, termed the "Ride Item's Coattails" attack, fabricates false click information to deceitfully establish associations between popular products and low-quality products, intending to mislead the e-commerce platform's recommendation system and promote the sales of substandard products. This article presents a recommendation system attack detection method based on the Deep Forest algorithm to address the challenges of these novel recommendation system attacks. Random forests are used for feature selection, aiming to filter crucial features and reduce feature redundancy. To tackle the issue of extreme class imbalance, a symmetric sampling technique based on k-means centroids is introduced. This approach addresses the incomplete noise filtering and sampling data comprehensiveness issues commonly found in undersampling algorithms. Considering the potential for even more imbalanced data in real-world scenarios, a combined strategy of undersampling and SMOTE resampling is used to handle imbalanced data. The proposed algorithm was trained on e-commerce "Ride Item's Coattails" attack identification data from Alibaba Cloud's Tianchi, which originates from genuine recommendation system attack data. The proposed method was compared with various deep learning and machine learning algorithms, such as DLMP and deep neural networks (DNN), for extensive validation. Experiments demonstrate that the proposed technique effectively meets the demands for attack detection. [ABSTRACT FROM AUTHOR]

A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users' feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. [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]

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]

Faults recovery has recently emerged as an important aspect of web service composition (WSC) testing, as it aims to minimize the impact of faults on system functionality through restoring the system's operation after a fault has occurred. However, most of the existing recommendation systems (RSs) tend to recommend frequently used services, which lack diversity and face inaccuracies due to incomplete or biased historical data. In addition, the focus of the existing RSs is on proposing fault handling models rather than recommending the best recovery strategy for handling faults, with most being code-based, thus not suitable for WSCs. Accordingly, due to the opaque nature of WSCs with hidden source code, model-based recovery methods are preferred. In this paper, the Fault Recovery Strategies RS for WSCs (F2RS-WSC) is proposed to recommend the best recovery strategy for handling emerging faults in the WSCs paradigm. The proposed system is a model-based system that recommends the best strategy for recovering faulty paths generated from service dependency graphs (SDGs) based on the faults' types, severity levels, faults' location, as well as the time at which faults may occur. The experimental results show that the time consumed by F2RS-WSC to recommend the optimum recovery strategy represents less than 3% of the SDG parsing time and 4% of the path validation time. In addition, its superior performance assures its accuracy and efficiency. Thus, it achieves accuracy levels between 70% and 88 % among multiple datasets. Moreover, its average precision, recall and f-measure values are 0.85,0.81 and 0.86 respectively. [ABSTRACT FROM AUTHOR]

Adventure tourism is a popular and growing segment within the tourism industry that involves, but is not limited to, hiking, running, and climbing activities. These activities attract investment from foreign travelers interested in practicing sports while exploring other countries. As a result, many software companies started developing Artificial Intelligence solutions to enhance tourists' outdoor adventure experience. One of the leading technologies in this field is recommender systems, which provide personalized recommendations to tourists based on their preferences. While this topic is actively being researched in some sports (running and hiking), other adventure sports disciplines have yet to be fully explored. To standardize the development of intelligence-based recommender systems, we conducted a systematic literature review on more than a thousand scientific papers published in decision support system applications in three outdoor adventure sports, such as running, hiking, and sport climbing. Hence, the main focus of this work is, firstly, to summarize the state-of-the-art methods and techniques being researched and developed by scientists in recommender systems in adventure tourism, secondly, to provide a unified methodology for software solutions designed in this domain, and thirdly, to give further insights into open possibilities in this topic. This literature survey serves as a unified framework for the future development of technologies in adventure tourism. Moreover, this paper seeks to guide the development of more effective and personalized recommendation systems. [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]

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]

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]

Study into decision support applications in healthcare, such as those dealing to diagnosis, forecasting, treatment planning, and various aspects of patient care, among other things, have seen a significant uptick in interest over the past few years. This surge in interest has been accompanied by a substantial increase in funding. This expansion has taken place as a consequence of developments in artificial intelligence and machine learning, as well as an increase in the availability of computer resources, as well as an increase in the quantity of data that is readily available. Every day, examples of studies that are both very intriguing and have been peer-reviewed are published in journals, which demonstrates the promise of this topic. At the same time, however, there are certain expectations and assumptions regarding the development, validation, and acceptance of such techniques that are unrealistic and at times overly optimistic. These expectations and assumptions exist at the same time as the unrealistic and overly optimistic ones. There are in-depth lectures provided on the topics of machine learning and deep learning, both of which are often used for health recommender systems, as well as how they are applied in the field of health informatics. The purpose of this study is to investigate many approaches that have been used in the area of health informatics in the past. [ABSTRACT FROM AUTHOR]

The widespread use of E-commerce websites has drastically increased the need for automatic recommendation systems with machine learning. In recent years, many ML-based recommenders and analysers have been built; however, their scope is limited to using a single filtering technique and processing with clustering-based predictions. This paper aims to provide a systematic year-wise survey and evolution of these existing recommenders and analysers in specific deep learning-based hybrid filtering categories using movie datasets. They are compared to others based on their problem analysis, learning factors, data sets, performance, and limitations. Most contributions are found with collaborative filtering using user or item similarity and deep learning for the IMDB datasets. In this direction, this paper introduces a new and efficient Hybrid Filtering based Recommendation System using Deep Learning (HFRS-DL), which includes multiple layers and stages to provide a better solution for generating recommendations. [ABSTRACT FROM AUTHOR]

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this article, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable Internet of Things (IoT) devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data, minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, and weight. Our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared with similar studies.1 Furthermore, our approach is novel compared with existing studies, as it does not require collecting and using users’ sensitive information. Thus, it preserves the users’ privacy. [ABSTRACT FROM AUTHOR]

We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set's optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores. [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]

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]

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]

Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users' preferences, and they build a model based on all the users' rates. This paper proposed an optimized clustering-based denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users' preferences using k-means algorithm combined with a nature-inspired algorithm (NIA) such as artificial fish swarm algorithm to determine the optimal initial centroids to cluster the users based on their similar preferences, and each cluster trains its own denoising autoencoder (DAE) model. The results proved that combining NIA with k-means gives better clustering results comparing with using k-means alone. OCB-DAE was trained and tested with MovieLens 1M dataset where 80% of it is used for training and 20% for testing. Root mean squared error (RMSE) score was used to evaluate the performance of the proposed model which was 0.618. It outperformed the other models that use autoencoder and denoising autoencoder without clustering with 38.5% and 29.5% respectively. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial Intelligence (AI) technology, especially, machine learning has received significant attention from many researchers and professionals in several domains. For instance, this technology has revolutionized industrials sectors, such as e-commerce, health, manufacturing, and entertainment, as well as, the educational sector. In this context, during the pandemic, educational institutions around the globe recognized the benefits of adopting remote learning and teaching based on many e-learning platforms. Hence, deploying virtual classrooms is not that easy, due to many constraints, such as time, technological cost, lack of interactivity of learners, and the unsuitability of the learner's cognitive abilities and learning style. To this end, understanding the learner's style and providing adapted and personalized content are the main concerns of today's e-learning systems. To this end, integrating efficient recommender systems into online learning environment is required in order to consider student's behaviors and preferences when recommending various learning materials. Thus, intelligent recommender systems are able to provide a personalized learning path in which the system adapts to the student's learning requirements and abilities. This review discusses the different applications, methods, and challenges of AI-based recommendation systems used to assist different stockholders in an online learning environment. [ABSTRACT FROM AUTHOR]

Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process. [ABSTRACT FROM AUTHOR]

To meet the demand for accurate recommendation and personalized learning in online education, an online course recommendation algorithm with multilevel fusion of user features and item features is proposed for content‐based recommendation systems applied to online education courses that have weak generalization ability and cannot cope well with data sparsity. The algorithm is improved on the deep learning recommendation algorithm named Wide & Deep Learning (WDL), which is a CTR (Click‐through rate prediction) prediction algorithm. MMF (Multi‐level Fusion Feature) uses collaborative filtering to replace linear methods in the wide part of WDL, introduces feature interactions to model user representations and item representations separately, and uses ResNet (Residual Network) ideas to improve deep neural network (DNN) in the deep part to reduce the performance degradation caused by overfitting. The experimental validation was conducted on the online education data set and the public data set MovieLens‐1M, and the AUC was improved by 1.21% and 1.46%, respectively. Meanwhile, the effect brought by this improved algorithm is interpretable. [ABSTRACT FROM AUTHOR]

Human aesthetics play a significant role in video game development, emotional‐aware robot design, online recommender systems, digital human, and other domains of research focusing on human‐computer interactions. Social network user recognition based on aesthetic preferences is an emerging research domain. In this paper, a novel deep learning architecture is proposed for multi‐modal audio‐visual person identification that combines audio and visual aesthetic features. A pre‐trained ResNet architecture is utilized to extract high‐level features from a set of user‐preferred audio and image samples. A novel deep learning‐based fusion technique called residual‐aided intermediate fusion (RAIF) is introduced in order to effectively merge the audio and visual features. The proposed RAIF method achieved an accuracy of 98% and a loss of 0.01 on a proprietary multi‐modal dataset, indicating its effectiveness in fusing audio and visual information. [ABSTRACT FROM AUTHOR]

Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction. By highlighting the contributions and challenges of recent research papers, this work aims to review state-of-the-art auto-encoder algorithms. Firstly, we introduce the basic auto-encoder as well as its basic concept and structure. Secondly, we present a comprehensive summarization of different variants of the auto-encoder. Thirdly, we analyze and study auto-encoders from three different perspectives. We also discuss the relationships between auto-encoders, shallow models and other deep learning models. The auto-encoder and its variants have successfully been applied in a wide range of fields, such as pattern recognition, computer vision, data generation, recommender systems, etc. Then, we focus on the available toolkits for auto-encoders. Finally, this paper summarizes the future trends and challenges in designing and training auto-encoders. We hope that this survey will provide a good reference when using and designing AE models. [ABSTRACT FROM AUTHOR]

In the futuristic Industry framework, user interactions with the product are seamlessly integrated with the product life cycle which results in Information overload. The shopbots were proposed in Industry 4.0 where the more focus was on process automation. In this research work, we propose recommender system for industry 5.0 which is based on collaboration of human beings and machines with more focus on user's personalization. Recommender system can be considered as an information filtering tool that provides suggestions to users about products, music, friend, topic, etc. This suggestion is based on the interest of users. Several research works have been carried out to improve recommendation accuracy by using matrix factorization, trust-based, hybrid-based, machine learning, and deep learning techniques. However, very few existing works have leveraged textual opinions for the recommendation to the best of our knowledge. Existing research works have focused only on numerical ratings, which do not reflect actual user behaviour. In this research work, sentiments of textual opinions are analysed for an in-depth analysis of user's behaviour. Recommendation accuracy is improved by using the proposed score Recop which is calculated from opinion sentiments. Furthermore, the sparsity issue is resolved by using our proposed approach. Amazon and Yelp review datasets are used for Experiment analysis. Mean absolute error (MAE) and root mean square error (RMSE) values are improved significantly using the proposed approach compared to the existing approaches. MAE and RMSE scores on the Yelp dataset are 0.85 and 1.51, respectively. Additionally, MAE and RMSE scores on the Amazon dataset are 0.66 and 0.93, respectively, which clearly reflects the significant contribution of our proposed approach. [ABSTRACT FROM AUTHOR]

Traditional recommendation systems are faced with two long-standing obstacles, namely data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Since the early 2010s, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey article, we first proposed a two-level taxonomy of cross-domain recommendation that classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty. [ABSTRACT FROM AUTHOR]

Employers couldn't find relevant candidates without great effort, and job seekers must spend a long time researching the right jobs. To fix this issue, recommender systems are an effective way to make talent and job research more effective and easier to complete. It is possible to analyze job offers and candidate profiles and make pertinent recommendations using techniques like the Recurrent Neural Network (RNN) approach. To accomplish this, we propose a model based on the classification of job profiles using a variant of RNN called LSTM ("Long short-term memory"). It is a long-term memory model based on a deep learning algorithm. To maintain the semantic meaning, first, the input of the LSTM model is transformed into a set of vectors using the doc2Vec method, which transforms a document into a vector of numbers. [ABSTRACT FROM AUTHOR]

In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented. [ABSTRACT FROM AUTHOR]