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A primary strategy for constructing click-through rate models based on deep learning involves combining a multi-layer perceptron (MLP) with custom networks that can effectively capture the interactions between different features. This is due to the widespread recognition that relying solely on a vanilla MLP network is not effective in acquiring knowledge about multiplicative feature interactions. These custom networks often employ product methods, such as inner, Hadamard, and outer products, to construct dedicated architectures for this purpose. Among these methods, the outer product has shown superiority in capturing feature interactions. However, the resulting quadratic form from the outer product operation limits the conveyance of informative higher-order interactions to the MLP. Efforts to address this limitation have led to models attempting to increase interaction degrees to higher orders. However, utilizing matrix factorization techniques to reduce learning parameters has resulted in information loss and decreased performance. Furthermore, previous studies have constrained the MLP's potential by providing it with inputs consisting of homogeneous outer products, thus limiting available information diversity. To overcome these challenges, we introduce DONN, a model that leverages a composite-wise bilinear module incorporating factorized bilinear pooling to mitigate information loss and facilitate higher-order interaction development. Additionally, DONN utilizes a feature-wise bilinear module for outer product computations between feature pairs, augmenting the MLP with combined information. By employing heterogeneous outer products, DONN enhances the MLP's prediction capabilities, enabling the recognition of additional nonlinear interdependencies. Our evaluation on two benchmark datasets demonstrates that DONN surpasses state-of-the-art models in terms of performance. [ABSTRACT FROM AUTHOR]

The continued expansion and development of the business requires great computing power and massive data storage systems. Cloud services deliver these resources in a simple, flexible and secure way. There is now a wide range of similar cloud services with different capabilities, which requires a recommendation system. Recommendation based on Quality of Service (QoS) is the first generation of service recommendation systems that only takes into account the rating information of all users without distinction. However, these systems suffer from many shortcomings, such as cold start and data sparsity issues, as well as poor accuracy and reliability of recommendation results. To address these issues and improve the quality of recommendations, a new generation of recommender systems has emerged, such as context‐aware, domain‐specific, and trust‐aware recommender systems. These systems now focus more on how to leverage social data generated from user interactions with each other in social networks to recommend more suitable and reliable services in response to user needs. Due to the importance of considering trust in cloud environments, this study aims to provide an overview of the research on trust‐based cloud service recommendation approaches proposed so far and highlights the current trend towards use new technologies such as deep learning to deal with certain challenges. [ABSTRACT FROM AUTHOR]

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models. [ABSTRACT FROM AUTHOR]

Deep neural networks are vulnerable to attacks, posing significant security concerns across various applications, particularly in computer vision. Adversarial training has demonstrated effectiveness in improving the robustness of deep learning models by incorporating perturbations into the input space during training. Recently, adversarial training has been successfully applied to deep recommender systems. In these systems, user and item embeddings are perturbed through a minimax game, with constraints on perturbation directions, to enhance the model's robustness and generalization. However, they still fail to defend against iterative attacks, which have shown an over 60% increase in effectiveness in the computer vision domain. Deep recommender systems may therefore be more susceptible to iterative attacks, which might lead to generalization failures. In this paper, we adapt iterative examples for deep recommender systems. Specifically, we propose a Deep Recommender with Iteration Directional Adversarial Training (DRIDAT) that combines attention mechanism and directional adversarial training for recommendations. Firstly, we establish a consumer-product collaborative attention to convey consumers different preferences on their interested products and the distinct preferences of different consumers on the same product they like. Secondly, we train the DRIDAT objective function using adversarial learning to minimize the impact of iterative attack. In addition, the maximum direction attack could push the embedding vector of input attacks towards instances with distinct labels. We mitigate this problem by implementing suitable constraints on the direction of the attack. Finally, we perform a series of evaluations on two prominent datasets. The findings show that our methodology outperforms all other methods for all metrics. [ABSTRACT FROM AUTHOR]

In the current traditional tourism recommendation systems, significant amounts of manpower and resources are required to manually identify the characteristics of resources, resulting in extremely poor economic benefits. To address this issue, this study proposes a smart tourism model based on deep learning and attention mechanisms. It uses a deep learning model to extract semantic information and improves it with the attention mechanism. This is to enable the model to take into account the complete meaning of the text and the association between individual words, thereby achieving a more comprehensive extraction of tourism resource features. The experiment showcases that the F 1-value of the algorithm proposed by us reached 0.961, the Recall value reached 0.958, the accuracy reached 0.980 and the area under the receiver operating characteristic curve reached 0.956. All parameters are superior to the comparison algorithm, and in practical application testing, its fitting degree reached 0.981. The above results indicate that the smart tourism proposed by us based on deep learning and attention mechanism has excellent performance in the field of tourism resource recommendation, which can effectively extract hidden features from the resources. This can also accurately push the tourism resources that users are interested in, which can effectively promote the integration and development of the tourism industry and the Internet, and has strong positive significance for economic development. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOC) are the largest initiative in eLearning, with the support of universities across the world. To increase course satisfaction in MOOCs, learners' must relate to the courses that best suit their needs and interests. The goal of recommendation systems is to suggest items to users based on their preferences and past behaviour. A course recommender system makes recommendations based on the similarity of courses and past interactions with the MOOC platform. With a huge volume of online courses on multiple learning platforms, it has been difficult for learners to identify the course of their interest. To address these challenges, a novel framework for hybrid MOOC course recommendations is proposed to recommend courses from multiple learning platforms. It uses web scraping techniques to collect course data from various MOOC providers, such as Coursera, Udemy, and edX platforms. With the real time dataset, a deep learning chatbot captures the personalized learning requirements of learners and recommends using a user-user collaborative approach with the valence aware dictionary and sentiment reasoner (VADER) for sentiment analysis. It enhances the accuracy of recommendations with an root-meansquare error (RMSE) value of 0.541. [ABSTRACT FROM AUTHOR]

The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS. [ABSTRACT FROM AUTHOR]

Tourism recommendation systems have tended to become popular in recent years. Due to the fact that tourism content is generally with the format of multimodal information, existing research works mostly ignored the fusion of various feature types. To deal with this issue, this paper resorts to multimodal fusion of semantic analysis and image comprehension, and proposes a novel deep learning-based recommender system for tourism routes. First, semantic analysis under tourism route search is conducted, in order to complete destination selection and process selection. Then, image comprehension of overall tourism route planning is conducted by establishing an end-to-end object recognition model. Finally, the previous two parts of characteristics are fused together to formulate an integrated recommender system with multimodal sensing ability. This thought is expected to bring a stronger ability for tourism route discovery. Empirically, operational efficiency and stability analysis are carried out on real-world data to evaluate the performance of the proposal. The experimental results show that it can achieve significant improvement in tourism route recommendation, can accurately capture user preferences, and can provide travel suggestions that meet user requirements. [ABSTRACT FROM AUTHOR]

In recent years, recommender systems—which provide personalized recommendations by analyzing users' historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these "black-box" systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users' trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models. [ABSTRACT FROM AUTHOR]

With the wide application of the intelligent news algorithm in the news industry, its recommendation mechanism has become the primary way for news consumers to obtain information. This study explores the intelligent news algorithm recommendation mechanism’s risk management measures and optimization schemes. Thus, people can get transparent news information more efficiently. Firstly, the study classifies and analyzes the risks of information filtering bias, information cocoon effect, and information bubble in intelligent news algorithm recommendation mechanism and collects and introduces large-scale news data as a data source. Secondly, the intelligent news algorithm recommendation model based on the convolutional neural network is constructed. The model uses word embedding technology to transform news articles into vector representations and trains the model to learn the feature representations of news articles and the correlation between them. Moreover, the loss function and weight of the model are adjusted to improve the diversity and balance of the recommendation results. Finally, simulation experiments are carried out to evaluate the model’s performance. The results reveal that the information diversity of the system model in this study is increased by 15%, and user satisfaction and the information quality index are increased by 10% and 7%. It proves the importance of diversified data sources, algorithm transparency and explainability, user feedback and participation, and balanced recommendation strategies to reduce risk and improve the performance of recommendation mechanisms. Therefore, the research results guide the practical application of the intelligent news algorithm recommendation mechanism and provide a reference for further improvement and optimization of the recommendation algorithm. [ABSTRACT FROM AUTHOR]

The Remote Sensing Image Change Captioning (RSICC) has recently emerged in the field of remote sensing image interpretation; it aims to automatically predict natural language captions of significant semantic changes in bi-temporal remote sensing images. Recent studies of RSICC have improved the accuracy of change captions of bi-temporal remote sensing images to a large extent. Nevertheless, there still remain challenges in multi-scale perception of ground objects and feature enhancement of bi-temporal remote sensing images. To address these challenges and further improve the accuracy of RSICC, a novel deep learning–based end-to-end scale-wised feature enhancement network (SFEN) is proposed in this paper. SFEN integrates four efficient blocks: 1) the siamese backbone network (SBN) to extract initial features of bi-temporal remote sensing images, 2) the siamese receptive field fusion (SRFF) block to explicitly capture multi-scale semantic information of ground objects in bi-temporal feature maps, 3) the siamese global feature enhancement (SGFE) block to adaptively enhance key information and filtering redundant features of bi-temporal feature maps in both channel and spatial dimensions, 4) the change caption decoder (CCD) to map bi-temporal feature maps into natural language. The SFEN aims to precisely capture significant semantic information of ground objects in bi-temporal remote sensing images and predict accurate change captions. Experimental results on LEVIR-CC dataset demonstrate our SFEN outperforms recent state-of-the-art (SOTA) approach in RSICC by 5.2% on CIDEr-D and achieves a new SOTA. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation's land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of "Spider" plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield. [ABSTRACT FROM AUTHOR]

Recommender systems are already being used by several biggest e-commerce websites to assist users in finding things to buy. A recommender system gains knowledge from a consumer and suggests goods from the available goods that will find most value. In this deep learning technique, the Hybrid Deep Collaborative Transformer (HDCT) method has emerged as a promising approach. However, it is crucial to thoroughly examine and rectify any potential errors or limitations in the optimization process to ensure the optimal performance of the HDCT model. This study aims to address this concern by thoroughly evaluating the HDCT method uncovering any underlying errors or shortcomings. By comparing its performance against other existing models, the proposed HDCT with Federated Learning method demonstrates superior recommendation accuracy and effectiveness. Through a comprehensive analysis, this research identifies and rectifies the errors in the HDCT model, thereby enhancing its overall performance. The findings of this study provide valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Data for the RS is collected from the Myntra fashion product dataset. By understanding and addressing the limitations of the HDCT method, businesses can leverage its advantages to improve customer satisfaction and boost their revenue. Ultimately, this research contributes to the ongoing advancements in e-commerce recommendation systems and paves the way for future improvements in this rapidly evolving domain. The suggested model's efficacy is assessed using metrics for MSE, MSRE, NMSE, RMSE, and MAPE. The suggested values in metrics are 0.2971, 0.2763, 0.4013, 0.3222, 0.2911 at a 70% learn rate and 0.2403, 0.2234, 0.3506, 0.2025, 0.2597 at an 80% learn rate, and the proposed model outperformed with the least amount of error. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method called AcoRec, which employs an enhanced version of Continuous Ant Colony Optimization for hyper-parameter adjustment and integrates a non-deterministic model to generate diverse recommendation lists. AcoRec is designed for cold-start users and long-tail item recommendations by leveraging implicit data from collaborative filtering techniques. Continuous Ant Colony Optimization is revisited with the convenience and flexibility of deep learning solid methods and extended within the AcoRec model. The approach computes stochastic variations of item probability values based on the initial predictions derived from a selected item-similarity model. The structure of the AcoRec model enables efficient handling of high-dimensional data while maintaining an effective balance between diversity and high recall, leading to recommendation lists that are both varied and highly relevant to user tastes. Our results demonstrate that AcoRec outperforms existing state-of-the-art methods, including two random-walk models, a graph-based approach, a well-known vanilla autoencoder model, an ACO-based model, and baseline models with related similarity measures, across various evaluation scenarios. These evaluations employ well-known metrics to assess the quality of top-N recommendation lists, using popular datasets including MovieLens, Pinterest, and Netflix. [ABSTRACT FROM AUTHOR]

Many individuals aspire to start their own businesses and achieve financial success. Before launching a business, they must decide on a location and the type of service to offer. This decision requires collecting and analyzing various characteristics of potential locations and services, such as average revenues and foot traffic. However, this process is challenging because it demands expert knowledge in data collection and analysis. To address this issue, we propose Neural Tensor Factorization (NeuralTF) and Explainable Neural Tensor Factorization (XNeuralTF). These methods automatically analyze these characteristics and predict revenues. NeuralTF integrates Tensor Factorization (TF) with Multi-Layer Perceptron (MLP). This integration allows it to handle multi-dimensional tensors effectively. It also learns both explicit and implicit higher-order feature interactions, leading to superior predictive performance. XNeuralTF extends NeuralTF by providing explainable recommendations for three-dimensional tensors. Additionally, we introduce two novel metrics to evaluate the explainability of recommendation models. We conducted extensive experiments to assess both predictive performance and explainability. Our results show that XNeuralTF achieves comparable or superior performance to state-of-the-art methods, while also offering the highest level of explainability. [ABSTRACT FROM AUTHOR]

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a renewed interest in Artificial Intelligence and its diverse frameworks. This research proposed a big data recommendation system that leverages two prominent deep learning frameworks which are PyTorch and TensorFlow to enhance collaborative filtering (CF) outcomes. The goal is to recommend more relevant products to users by incorporating textual review comments and combining various attributes. The study explores two recommendation system approaches within each framework. In the PyTorch framework, two collaborative filtering models were developed: one without incorporating user review text and the other with the inclusion of text from user reviews. Similarly, for the TensorFlow framework, two recommendation models were created: one without utilizing user review text, and the other with the integration of text reviews. The dataset, sourced from the Amazon website, comprised over 600,000 ratings and reviews. The outcomes showed significant enhancements by employing the proposed text-based method in both PyTorch and TensorFlow frameworks and addressed challenges such as dynamic preferences and data sparsity. Finally, the study provides a comparative analysis of the advantages and drawbacks of each utilized framework. [ABSTRACT FROM AUTHOR]

Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS. [ABSTRACT FROM AUTHOR]

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model's insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. [ABSTRACT FROM AUTHOR]

Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various recommendation techniques, collaborative filtering has shown tremendous performance by using the rating patterns of users. Traditional collaborative filtering, matrix factorization, and deep matrix factorization are the most representative collaborative filtering techniques. However, despite their extensive utility, the selection of the method which provides better recommendation performance is still a major concern in multi-criteria recommender systems. Most recommender systems (RSs) work only on the single criterion rating, i.e., the overall rating. Single-criterion collaborative filtering (CF) generates less reliable recommendations because it suffers from correlation-based similarity problems. However, predictions based on multiple criteria have proven more accurate. This paper compares traditional collaborative filtering, matrix factorization and deep matrix factorization in recommender systems on multi-criteria datasets. We describe details of these techniques in various aspects of recommendation quality, such as how those methods handle cold-start problems, which typically happen in collaborative filtering. We performed several experiments extensively over two real-world datasets to evaluate the performance of each method in terms of qualitative and quantitative measures and observe that deep matrix factorization techniques is superior to all other techniques. [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]

Improving the accuracy of recommendation systems is a hot research topic, and existing methods have not considered the differences in various types of data. To address this issue, we propose a collaborative filtering recommendation method that combines evaluation relationships and emotional relationships. The emotional relationship is derived from implicit feedback data, and we can exploit the neural network to learn the emotional relationship between users and items. The evaluation relationship is derived from explicit feedback data; similarly, we can exploit the neural network to learn the evaluation relationship between users and items. To obtain emotional and evaluation relationships, we design vector representation and interaction functions based on explicit and implicit feedback data. Moreover, we propose that deep neural networks can be used to integrate evaluation and emotional relationships. To effectively integrate emotional and evaluation relationships, we design multiple data fusion strategies, loss functions, and training strategies. Experimental studies were conducted on five benchmark datasets, and the results show that the collaborative filtering recommendation method based on emotional evaluation relationships can further improve the accuracy of recommendation systems. [ABSTRACT FROM AUTHOR]

To recommend useful information to users more efficiently, this paper proposes a dual-path recommendation algorithm which combines multilayer Convolutional Neural Network (CNN) and attention-enhanced long short-term memory network (Attention-LSTM). Firstly, the matrix factorisation technique is used for learning the long-term preferences of users. Secondly, a dual-path network based on CNN and LSTM is constructed to perform feature extraction on the rating matrix. The dual-path network can learn the long-term preferences of users while capturing their dynamic preferences in changing preferences. The algorithm is tested on the public dataset MovieLens-1M, and the MAE value reflects the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous in modern society, facilitating various applications ranging from essential services to online entertainment. To ensure that quality of service (QoS) standards are met, cloud frameworks must be capable of adapting to the changing demands of users, reflecting the societal trend of collaboration and dependence on automated processing systems. This research introduces an innovative approach for link prediction and user cloud recommendation, leveraging nano-grid applications and deep learning techniques within a cloud computing framework. Heuristic graph convolutional networks predict data transmission links in cloud networks. The trust-based hybrid decision matrix algorithm is then employed to schedule links based on user recommendations. The proposed model and several baselines are evaluated using real-world networks and synthetic data sets. The experimental analysis includes QoS, mean average precision, root mean square error, precision, normalized square error, and sensitivity metrics. The proposed technique achieves QoS of 73%, mean average precision of 59%, root mean square error of 73%, precision of 76%, normalized square error of 86%, and sensitivity of 93%. The findings suggest that integrating nano-grid and deep learning techniques can effectively enhance the QoS of cloud computing frameworks. [ABSTRACT FROM AUTHOR]

The exponential rise in the popularity of social networks in recent years has led to a corresponding surge in online data, creating a vast pool of information. This growing dataset has captivated the interest of researchers, compelling them to devise recommendation algorithms designed to navigate through this immense sea of data to establish meaningful friendships. Despite the success of CF, there is a notable gap in the related research, as deep learning's potential to generate friendship recommendations in social networks using CF has been neglected. This paper seeks to fill this gap by introducing a novel, collaborative friendship recommendation system. The proposed system leverages the power of Neural Collaborative Filtering (NCF) to offer users a platform for discovering new friends who are highly likely to engage interactively. To underscore the effectiveness of our model, we present experimental results using a real dataset, showcasing the system's capability to deliver accurate and valuable friend recommendations. [ABSTRACT FROM AUTHOR]

Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion. Firstly, we use small channel convolution to construct shallow feature extractionmodule (SFEM) to extract low-level feature information of cracks in cement pavement images, in order to obtainmore information about cracks in the shallowfeatures of images. In addition,we construct large kernel atrous convolution (LKAC) to enhance crack information, which incorporates coordination attention mechanism for non-crack information filtering, and large kernel atrous convolution with different cores, using different receptive fields to extract more detailed edge and context information. Finally, the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module, and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map. We evaluate our method on three public crack datasets: DeepCrack, CFD, and Crack500. Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods, which achieves Precision (P) 87.2%, Recall (R) 87.7%, and F-score (F1) 87.4%. Thanks to our lightweight crack detectionmodel, the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M. This advancement also facilitates technical support for portable scene detection. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) primarily rely on user feedback as a core foundation for making recommendations. Traditional recommenders predominantly rely on historical data, which often presents challenges due to data scarcity issues. Despite containing a substantial wealth of valuable and comprehensive knowledge, user reviews remain largely overlooked by many existing recommender systems. Within these reviews, there lies an opportunity to extract valuable insights, including user preferences and contextual information, which could be seamlessly integrated into recommender systems to significantly enhance the accuracy of the recommendations they provide. This paper introduces an innovative approach to building context-aware RS, spanning from data extraction to ratings prediction. Our approach revolves around three essential components. The first component involves corpus creation, leveraging Dbpedia as a data source. The second component encompasses a tailored named entity recognition (NER) mechanism for the extraction of contextual data. This NER system harnesses the power of advanced models such as bidirectional encoder representations from transformers (BERT), bidirectional long short term memory (Bi-LSTM), and bidirectional conditional random field (Bi-CRF). The final component introduces a novel variation of factorization machines for the prediction of ratings called contextual factorization machines. Our experimental results showcase robust performance in both the contextual data extraction phase and the ratings prediction phase, surpassing the capabilities of existing state-of-the-art methods. These findings underscore the significant potential of our approach to elevate the quality of recommendations within the realm of contextaware recommender systems. [ABSTRACT FROM AUTHOR]

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Recognizing consumer interests and satisfaction is crucial for organizations to thrive in a competitive market. A strong marketing strategy aids companies in reaching customers and building a reputation for their products. Presently, numerous internet platforms employ recommender systems on a commercial basis. Customer preferences are generally obtained behaviours of users from online shopping which is used to recommend the products. Deep Learning (DL) models have been used for efficient customer preference recommendation, but these models don't consider secondary information such as Knowledge Graph (KG) and comment information. Hence, in this paper, Time Aware Attention and KG embedded Deep Network for Recommendation (TAK-DepNR) is proposed by combining Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN). In this method, the user similarity, item similarity and latent period are modelled using Matrix Factorization (MF) model. The MF generates long and short preferences which are then fed into the CNN and GCN models to produce dynamic user preferences. The CNN extracts higher-level characteristics among the user and item from MF, then reduces to lower-order features. The GCN aids to train the interpretation of each node and encodes the KG based Global Convolutional Feature Map (KGGCFM) for CNN through the shared features. This creates a major link between the products and allowing for more extensive item feature description for user preference graph. The CNN will be integrated with GCN using its squeezed units to learn the high-level feature relations between recommendation systems and KG entities. Finally, dynamic user preferences from CNN and GCN are passed into Fully Connected (FC) layer constructed network to recommend the items. The experimental results show that the TAK-DepNR realizes average precision of 89% and 88% compared to the other models like Immersive Graph Neural Network (IGNN), Attentional Factorization Machine with Review- Based User-Item Interaction (AFMRUI), Item Collaborative Filtering with RNN (ICFRNN), Deep Reinforcement Recommender System with Maximum pooling layers (DRR-Max) and Sentiment Analysis with Bidirectional Long Short Term Memory (SA-BiLSTM) on Amazon Digital Music Dataset and Book Crossing Dataset. [ABSTRACT FROM AUTHOR]

The student feedback data possesses to be the fundamental influencers of decision-making process in diverse applications. The performance prediction based on student's feedback about the educational institution helps for better solution recommendation. The automated solution recommendation based on student's feedback extensively support the educational institution to make better decisions for improvisation. In most of the existing research works, the performance can be analysed, but suitable solution recommendation is not provided. Also, the existing recommendation works fail to generate accurate outcomes, consumes more time with higher error rates. Hence on diminishing the existing issues, this research work presents a Data science-based solution Recommendation model based on hybrid deep learning approaches. Pre-processing, feature extraction, feature clustering, performance prediction, and recommendation are the steps in the suggested model. In this research, the student feedback data is collected from Kaggle source and some of the attributes are added manually. Pre-processing techniques for the text data include stop-word-removal, tokenization, case-folding, and stemming. The features are extracted using Enhanced Lexicon bidirectional encoder representations from transformers (ELexBert) model. The significant attributes are selected using Adaptive Coati optimization (ACoaT) algorithm. The selected features are clustered based on feature similarity using Upgraded density based k-means clustering (Uden_KMC) model. A new type of hybrid deep learning model known as Channel Block Densnet with Dilated Convolution BiLSTM (ChaBDBiL) is employed for recommending better decisions. The recommendation performances using feedback data are evaluated using PYTHON where the overall accuracy of 98.19%, specificity of 98.25%, F1 score of 91.28%, sensitivity of 97.41% and Kappa score of 91.28% are obtained. [ABSTRACT FROM AUTHOR]

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

In practice, the most important aspect has been to suggest items of interest to users based on their prior choices. In recent decades, recommendation systems have been highly successful in achieving this goal by using a variety of methods. Profile reliance is generally used as a critical element in traditional recommendation systems. Considering this impact is difficult to address, academics have been interested in analyzing user behavior in an ongoing session. Based on this insight, Researchers developed session-based recommendation to address this challenge of recommending products to users based on their ongoing interactions. Session-based Recommendation is a sequential method of determining user-item interactions that doesn't require access to the user's profile information or their entire history of preferences. Due to its capacity to analyze non-linear data, a novel gradient descent-based temporal convolution neural network is developed in this study to meet the session-based recommendation. Non-linear distortion, knowledge discovery, sequence mapping, and adaptability are the key benefits of adopting Deep Learning for recommendation systems. Utilizing the box-cox transformation paradigm, the primary data transformation stage is implemented to change the invalid data into valid information. Second, a novel gradient descent temporal convolution neural network (GDTCNN), created specifically for e-commerce applications, is proposed to carry out the session-based recommendations. When compared to the current methods, the suggested model has a better accuracy rating of 99.49, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

In order to provide the personalized news recommendation for users more efficiently, the personalized recommendation system combined with deep network was investigated. Based on the deep network, a news recommendation system for users was designed. By means of neural network and aggregating users' interest characteristics, users' personalized needs for news recommendation was met. In order to solve the problem of inaccurate construction of users' interest preference characteristics, a personalized news method based on users' search records and interest preference was proposed. By constructing exogenous user interest preferences from user search records, the final recommendation list was generated by using the fusion method of the two preferences. Because the traditional recommendation algorithm ignored time series of the users' browsing behaviors, an improved circulation model of neural network algorithm was proposed. The parallel convolution neural network based on attention was used to aggregate characteristics of users' interest and the recursive neural network based on attention mechanism was used to explore hidden time series characteristics. At the same time, it was tested on real news data sets and the results showed that this method had a good recommendation effect. [ABSTRACT FROM AUTHOR]

With the massive amount of data available on the Internet, many users face problems when they need access to the information and goods they need. Adapting information to user needs has become a complex and time-consuming process. Recommender systems are powerful tools for guiding users in electronic settings to information, services, and goods of interest. With the ability to identify users and predict their preferences, recommender systems can extract information likely to be of interest to users from vast amounts of data and save time and energy by providing them with relevant recommendations. Therefore, in this paper, a framework for a recommender system is proposed, which consists of four main phases. In the first phase, the preprocessing operation is performed. The purpose of this phase is to create an ontology model for products based on the convolutional neural network method. Data collection operations are carried out in the second phase. The purpose of this phase is to receive and store user information based on behavior and different criteria. The third phase is related to creating an ontology. The purpose of this phase is to create an ontology model for user behavior using OWL libraries. Finally, in the last phase, finding similarities and presented a proposal. The purpose of this phase is the necessary calculations of the proposed framework in the similarity finding section using ontology models. Simulation results show that the proposed framework measures MAE (more than 10 and 15%), RMSE (more than 12 and 16%), precision (less than 10 and 13%), recall (more than 25 and 28%), F1-score (higher than 25 and 30%) and Score of User (higher than 15 and 17%) outperform two related approaches, namely CF and CF+ Ontology, respectively. [ABSTRACT FROM AUTHOR]

Click‐through rate (CTR) prediction is a pivotal challenge in recommendation systems. Existing models are prone to disturbances from noise and redundant features, hindering their ability to fully capture implicit and higher‐order feature interactions present in sparse feature data. Moreover, conventional dual‐tower models overlook the significance of layer‐level feature interactions. To address these limitations, this article introduces Gate‐enhanced Multi‐space Interactive Neural Networks (GMINN), a novel model for CTR prediction. GMINN adopts a dual‐tower architecture in which a multi‐space interaction layer is introduced after each layer in the dual‐tower deep neural network. This layer allocates features into multiple subspaces and employs matrix multiplication to establish layer‐level interactions between the dual towers. Simultaneously, a field‐aware gate mechanism is proposed to extract crucial latent information from the original features. Experimental validation on publicly available datasets, Criteo and Avazu, demonstrates the superiority of the proposed GMINN model. Comparative analyses against baseline models reveal that GMINN substantially improves up to 4.09% in AUC and a maximum reduction of 7.21% in Logloss. Additionally, ablation experiments provide further validation of the effectiveness of GMINN. [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]

Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics. Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis. Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual. Results: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems. Conclusion: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations. [ABSTRACT FROM AUTHOR]

Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model. [ABSTRACT FROM AUTHOR]

Recommendation systems are crucial in navigating the vast digital market. However, user data's dynamic and non-stationary nature often hinders their efficacy. Traditional models struggle to adapt to the evolving preferences and behaviours inherent in user interaction data, posing a significant challenge for accurate prediction and personalisation. Addressing this, we propose a novel theoretical framework, the non-stationary transformer, designed to effectively capture and leverage the temporal dynamics within data. This approach enhances the traditional transformer architecture by introducing mechanisms accounting for non-stationary elements, offering a robust and adaptable solution for multi-tasking recommendation systems. Our experimental analysis, encompassing deep learning (DL) and reinforcement learning (RL) paradigms, demonstrates the framework's superiority over benchmark models. The empirical results confirm our proposed framework's efficacy, which provides significant performance enhancements, approximately 8% in LogLoss reduction and up to 2% increase in F1 score with other attention-related models. It also underscores its potential applicability across accumulative reward scenarios with pure reinforcement learning models. These findings advocate adopting non-stationary transformer models to tackle the complexities of today's recommendation tasks. [ABSTRACT FROM AUTHOR]

Recent advancements in Natural Language Processing (NLP) have made sentiment analysis an essential component of a variety of NLP jobs, including recommendation systems, question answering, and business intelligence products. While sentiment analysis research has, to put it mildly, been widely pursued in English, Telugu has barely ever attempted the task. The majority of research works concentrate on analysing the sentiments of Tweets, news, or reviews containing Hindi and English words. There is a growing interest among academics in studying how people express their thoughts and views in Indian languages like Bengali, Telugu, Malayalam, Tamil and so on. Due to a paucity of labelled datasets, microscopic investigation on Indian languages has been published to our knowledge. This work suggested a sentence-level sentiment analysis on multi-domain datasets that has been collected in Telugu. Deep learning models have been used in this work because it demonstrates the significant expertise in sentiment analysis and is widely regarded as the cutting-edge model in Telugu Sentiment Analysis. Our proposed work investigates a productive Bidirectional Long Short-Term Memory (BiLSTM) Network and Bidirectional GRU Network (BiGRU) for improving Telugu Sentiment Analysis by encapsulating contextual information from Telugu feature sequences using Forward-Backward encapsulation. Further, the model has been deployed by merging the domains so as to predict the accuracy and other performance metrics. The experimental test findings show that the deep learning models outperform when compared with the baseline traditional ML methods in four benchmark sentiment analysis datasets. There is evidence that the proposed sentiment analysis method has improved precision, recall, F1-score and accuracy in certain cases. The proposed model has achieved the F1-score of 86% for song datasets when compared with the other existing models. [ABSTRACT FROM AUTHOR]

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA), the process of determining the emotional tone behind a piece of text, has gained significant importance in various domains, including marketing, customer feedback analysis, and social media monitoring. Traditional SA models often face challenges handling diverse global datasets due to language variations, cultural nuances, and context differences. This paper proposes an Ensemble Recommendation System (ERS) approach to address these challenges and improve SA accuracy. The ERS combines Gated Attention-Based Recurrent Networks (GARNET) Steps with Transfer Learning. The ERS leverages the power of ensemble learning, combining multiple sentiment analysis models trained on global datasets from diverse linguistic backgrounds. The ERS can provide more robust and accurate sentiment classifications by aggregating predictions from these models. Additionally, the system utilizes a recommendation mechanism that dynamically selects the most suitable model based on the characteristics of the input text, such as language, tone, and context. In this paper, an advanced pre-trained model DistilBERT is used to train the selected datasets and apply transfer learning. Transfer learning is used to send the features extracted from training and sends them to the proposed approach ERS. Second, we design and train multiple state-of-the-art SA models integrated to handle specific linguistic attributes while also considering the cultural biases present in the datasets. Third, we introduce the ERS recommendation mechanism, which enhances the system's performance and optimizes computational resources. For the effectiveness of the proposed ERS, extensive experiments were conducted on benchmark datasets and comparing its performance with individual sentiment analysis models and conventional ensemble techniques. The performance of the proposed approach achieves superior sentiment classification accuracy, especially when dealing with challenging global datasets. Finally, ERS presents a promising solution for deep sentiment analysis over diverse global datasets. Finally, the ERS also focused on finding the popular items or products recommended by the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.

Texture describes the unique features of an image. Therefore, texture classification is a crucial task in computer vision. Various CNN-based deep learning methods have been developed to classify textures. During training, the deep-learning model undergoes an end-to-end procedure of learning features from low to high levels. Most CNN architectures depend on high-level features for the final classification. Hence, other low- and mid-level information was not prioritized for the final classification. However, in the case of texture classification, it is essential to determine detailed feature information within the pattern to classify textures as they have diversity and irregularity in images within the same class. Therefore, the feature information at the low- and mid-levels can also provide meaningful information to distinguish the classes. In this study, we introduce a CNN model with a feature retention module (FRM) to utilize features from numerous levels. FRM maintains the texture information extracted at each level and extracts feature information through filters of various sizes. We used three texture datasets to evaluate the proposed model combined with the FRM. The experimental results showed that learning using different levels of features together assists in improving learning performance more than learning using high-level features. [ABSTRACT FROM AUTHOR]

Motivation The rapid expansion of Bioinformatics research has led to a proliferation of computational tools for scientific analysis pipelines. However, constructing these pipelines is a demanding task, requiring extensive domain knowledge and careful consideration. As the Bioinformatics landscape evolves, researchers, both novice and expert, may feel overwhelmed in unfamiliar fields, potentially leading to the selection of unsuitable tools during workflow development. Results In this article, we introduce the Bioinformatics Tool Recommendation system (BTR), a deep learning model designed to recommend suitable tools for a given workflow-in-progress. BTR leverages recent advances in graph neural network technology, representing the workflow as a graph to capture essential context. Natural language processing techniques enhance tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, showcasing its potential to streamline scientific workflow construction. Availability and implementation The Python source code is available at https://github.com/ryangreenj/bioinformatics_tool_recommendation. [ABSTRACT FROM AUTHOR]

Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work. [ABSTRACT FROM AUTHOR]