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Recommender system helps address information overload problem and satisfy consumers' personalized requirement in many applications such as e-commerce, social networks, and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks but usually ignore how to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. A few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players' preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network–based method is designed to model players' multi-source heterogeneous information, including the players' historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness. [ABSTRACT FROM AUTHOR]

A recommendation system (RS) can help overcome information overload issues by offering personalized predictions for users. Typically, RS considers the overall ratings of users on items to generate recommendations for them. However, users may consider several aspects when evaluating items. Hence, a multi-criteria RS considers n-aspects of items to generate more accurate recommendations than a single-criteria RS. This research paper proposes two deep encoder–decoder models based on shared learning for a multi-criteria RS, multi-modal deep encoder–decoder-based shared learning (MMEDSL) and multi-criteria deep encoder–decoder-based shared learning (MCEDSL). MMEDSL employs the shared learning technique by concentrating on the multi-modality concept in deep learning, while MCEDSL focuses on the training process to apply the shared learning technique. The shared learning captures useful shared information during the learning process since the multi-criteria may have hidden inter-relationships. A set of experiments were conducted to compare the proposed models with recent baseline approaches. The Yahoo! Movies multi-criteria dataset was utilized. The results demonstrate that the proposed models outperform other algorithms. In addition, the results show that integrating the shared learning technique with the RS produces precise recommendation predictions. [ABSTRACT FROM AUTHOR]

A recommender system is an emerging personalization strategy in web applications to deal with information overload. Most recommender systems suggest items to users based on a single criterion, i.e., overall ratings. However, multi-criteria ratings are used for modeling more complex preferences of users. The incorporation of criteria ratings can lead to generating more reliable recommendations for users. Despite the success of multi-criteria methods, there is a need to be further optimized to handle popular issues, e.g., sparsity and cold-start. This research study presents a novel multi-criteria collaborative filtering recommender system based on optimization algorithms. The proposed method consists of an adaptable predictive model called multi-criteria inheritance-based prediction (MC-INH-BP). MC-INH-BP allows the customizing of the predictive model to suit the user context. Also, we propose a user profiling method called dynamic user interest print (D-UIP). The D-UIP stores the dynamic preferences of the users. The use of D-UIP reduces the impact of four challenges, critical users, tolerant users, dynamic opinion of the recurring users, and dynamic quality of the item. A set of experiments are conducted to compare MC-INH-BP with other single-criterion and multi-criteria collaborative filtering methods. The benchmark dataset, HotelExpedia, is used. The results prove the capability of MC-INH-BP to achieve better prediction accuracy regardless of the current context of the user. Besides, the results reveal that MC-INH-BP mitigates the cold start and sparsity issues. [ABSTRACT FROM AUTHOR]

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS. [ABSTRACT FROM AUTHOR]

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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]

Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers' requirements is challenging. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack a more comprehensive analysis of the connections between the literature, the domain knowledge provided, and the hotspot information expressed in the literature. Previous models make it impossible to locate the appropriate documents for domain literature. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a recommendation model termed PRHN. Inputs of the model are the hotspot information network and the domain knowledge graph, which both were developed during the preceding research phase. After the query terms are extracted and the associated heterogeneous literature networks are formed, they are aggregated in a uniform hidden space. Similarity with the candidate set is determined to transform the search problem into a TOP N recommendation problem. Compared to state-of-the-art models, results generated by PRHN on public available datasets show improvement in HR and NDCG. Concretely, results on the metallurgical literature dataset are more conspicuous, with more remarkable improvement in HR and NGCC by approximately 4.5% and 4.2%. [ABSTRACT FROM AUTHOR]

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In the Internet era, where information and communication technologies (ICT) allow data exchange, new tools able to select the correct data are needed. In this field, Recommender Systems have a prime location. The recommendation methods take many forms based on the information exploited in order to provide rating forecasts. However, all of them can choose the correct information to support users. Indeed, these methods allow for overcoming the information overload problem. This paper introduces the theoretical bases of a novel recommendation method defined Rating Singular Value Decomposition (RSVD). RSVD is a Content-Based method that exploits the Singular Value Decomposition properties in order to calculate rating forecasts. This method aims to elaborate the users and items profile to obtain matrices related to ones obtained in Collaborative Filtering methods that exploit Singular Value Decomposition. The accuracy of RSVD is compared with the accuracy of Collaborative Filtering methods, and a study on the sparsity problem is performed. The results obtained are promising. [ABSTRACT FROM AUTHOR]

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the 'most' similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing number of smart objects and devices which are connected to the Internet leads to the introduction of numerous IoT services. However, due to the overloading of information, many users suffer from several difficulties in obtaining useful and relevant services that meet their needs and interests. Recommender Systems (RSs) provide an efficient solution to this complex problem. Yet, the existing IoT recommender systems are based on objects owned by the users themselves because it is a serious problem, in IoT recommender systems, to find alternative sources of the missing data to fulfill the service requirements. Whereas, the IoT data generated by various objects is inefficiently used. An efficient IoT service recommendation can be reached based on Social IoT (SIoT) using the generated data by various IoT devices owned by friends and friends of friends. In this paper, we propose a hybrid technique that combines implicit collaborative filtering and ontology to recommend personalized IoT services to users. Ontology is used for modeling the SIoT, where we incorporate the social relationships among objects into the recommendation process alongside the ratings while collaborative filtering predicts ratings and generates recommendations. The evaluation results show that our proposed recommendation technique outperforms the collaborative filtering using SIoT on its own without SIoT in terms of personalization and recommendation accuracy. [ABSTRACT FROM AUTHOR]

With the development of the internet and the exploding data, the recommendation algorithm is an effective way to solve information overload has been widely concerned. Recently, some researchers have introduced review information to improve recommendation performance. Previous methods typically combine the reviews for a given user or item into a single long document and then extract features from the document level. However, such methods train the model on aggregated historical reviews over time. There may be many conflicting semantics or vocabularies, so scalability and accuracy may be affected. This paper proposes an attentive convolutional neural network representing document and sentence(ACNNDS) for rating prediction. The parallel network includes the sentence-level and document-level reviews, which extract user and item latent expressions from aggregated documents. The importance of sentences and words in reviews is different for different users. We established sentence-level and document-level attention mechanisms to capture the critical sentences and words from reviews to solve this problem. The experimental results show that the ACNNDS boosts recommendation performance, which is significantly better than comparing methods. [ABSTRACT FROM AUTHOR]

In recent years, the ever-growing contents (movies, clothes, books, etc.) accessible and buyable via the Internet have led to the information overload issue and therefore the item targeting problem. Indeed, the huge mass of contents complexifies the identification of items fitting users' expectations. As powerful filtering tools, recommender systems efficiently alleviate the item targeting issue. Collaborative filtering-based methods are among the most influential algorithms adopted in recommender systems. Among collaborative filtering-based methods, model-based approaches are widely used in recent powerful recommendation methods. Due to its efficiency, the matrix factorization technique is spreadly employed in model-based approaches. However, those methods badly deal with issues such as data sparseness and cold-start problems that severely affect the recommendation quality. To overcome these limitations shown by state-of-the-art methods, we propose in this paper a recommender approach that couples the effectiveness of an enhanced matrix factorization technique to the power of a deep neural network model. In the first step, the user's latent factors and item latent factors are extracted from a doubly-regularized matrix factorization process. Thereafter, those latent factors are used to feed a deep learning structure in a forward-propagation process, and a normalized cross-entropy method is used to increase the precision of the deep neural network through a backpropagation process. The end prediction is made by combining results from the matrix factorization step and the deep neural structure. Extensive experiments are conducted on real-world datasets and show that our proposal outperforms other methods in terms of prediction accuracy and recommendation quality. [ABSTRACT FROM AUTHOR]

Recommender systems can effectively alleviate the problem of information overload caused by the rapid development of the Internet. However, the occurrence of shilling attacks restricts the healthy development of recommender systems. Therefore, how to detect shilling attacks accurately and efficiently is an important problem in the field of recommender systems security. The existing detection methods usually design hand-crafted detection features based on expert knowledge or automatically learn features from a single perspective using deep learning, then the attack users are identified according to the extracted features by hard classification, resulting in the poor detection performance. By automatically learning features from multiple perspectives and introducing a hesitant fuzzy decision, a novel detection method based on CNN and hesitant fuzzy set was proposed and named CNN-HFS. Firstly, for each user, three behavior matrices were extracted from the perspectives of rating, preference and rating time, respectively. To reduce the influence of data sparse, these matrices were scaled by bicubic interpolation to correspondingly obtain a dense rating matrix, a dense preference matrix and a dense time matrix. Next, each scaling matrix of users was regarded as an image, and three different CNN classifiers were trained based on these scaling matrices in three different views respectively. For each user, three membership degrees to the classifier of attack users were calculated. Finally, a fuzzy hesitant set was introduced to make a comprehensive decision, and the attack users were identified according to the decision results. To validate the effectiveness of the proposed CNN-HFS, the extensive experiments were conducted on the MovieLens 1M and Amazon datasets. The evaluation metrics of precision, recall and F1-measure were used to compare the proposed method with SVM-TIA, CoDetector, CNN-SAD, SDAEs-PCA, CNN-R, CNN-P and CNN-T. The experimental results showed that the proposed method is superior to seven baseline methods in terms of three detection metrics and achieves an excellent detection performance under various attacks. [ABSTRACT FROM AUTHOR]

The prompt progression of web data and the number of web visitors creates a latent information overload problem and complicates data mining to select the right items on the web. E-commerce websites and applications manage information overload using several information filtering techniques such as personalized recommendation systems. The recommendation system creates a list of products to helps users. The proposed NOMINATE methodology offers characteristic values for definite elements from the LOD source as input for matrix factorization. This helps NOMINATE to retrieve user-specific functions. Subsequently studying the functions, the proposed approach produces an enhanced cluster of consumers by the weight of every user above the desired values of the element characteristics. For this, the NOMINATE methodology uses the particle swarm optimization (PSO) algorithm and k-means clustering algorithm. Using clustered results, the NOMINATE approach estimatiesthe centrality of proximity among other users in the cluster and identifies an experienced user for each cluster. In addition, the proposed recommendation scheme produces a set of characteristic values for each user based on the adept information of the users in the respective clusters. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Recommender system is considered to be widely utilized in diversified domain for the purpose of effectively handling information overload. But, recommender systems are prone to vulnerabilities that are significantly exploited by malicious attacks. In particular, shilling attack is determined to crucial in the recommender system due to its openness characteristics and data dependence. OBJECTIVES: Authors focused on detecting shilling attack by using hybrid deep learning techniques. METHODS: Hybrid CNN and RNNs-based shilling attack framework is proposed for shilling attack detection based on the selection of dynamic features for attaining maximized detection accuracy. RESULTS: The proposed CNN-RNNs-based shilling attack framework was determined to improve the recall with different filler size under Netflix dataset by 4.48% and 6.14%, better than the benchmarked HDLM and RMRA frameworks. The proposed CNN-RNNs-based shilling attack framework was determined to minimize the false positive rate by 4.82% and 5.94%, better than the benchmarked HDLM and RMRA frameworks. CONCLUSION: This framework integrated user popularity and rating-based indicators in order to consider the deviations that happens, when the users select items. It also included information entropy for dynamically choosing the detection indicators in order to improve the reliability in attack detection. It was proposed with three different attack detection models that contextually handles different shilling attacks. [ABSTRACT FROM AUTHOR]

As there is a huge amount of information on the Internet, people have difficulty in sorting through it to find the required information; thus, the information overload problem becomes a significant issue for users and online businesses. To resolve this problem, many researchers and applications have proposed recommender systems, which apply user-based collaborative filtering, meaning it only considers the users' rating history to analyze their preferences. However, users' text data may contain users' preferences or sentiment information, and such information can be used to analyze users' preferences more precisely. This work proposes a method called the aspect-based deep learning rating prediction method (ADLRP), which can extract the aspects, sentiment, and semantic features from users' and items' reviews. Then, the deep learning method is used to generate users' and items' latent factors. According to these three features, the matrix factorization method is applied to make rating predictions for items. The experimental results show that the proposed method performs better than the traditional rating prediction methods and conventional artificial neural networks. The proposed method can precisely and efficiently extract the sentiments and semantics of each aspect from review texts and enhance the prediction performance of rating predictions. [ABSTRACT FROM AUTHOR]

A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources. [ABSTRACT FROM AUTHOR]

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions. [ABSTRACT FROM AUTHOR]

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items' latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms. [ABSTRACT FROM AUTHOR]

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users. [ABSTRACT FROM AUTHOR]

With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user's historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user's next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user's short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user's check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user's long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user's long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The evolution of the Internet of Things (IoT) accelerates the augmentation of data present on the Internet and possibilities for connections to the more dynamic and heterogeneous devices to the Internet. Recommendation technologies have proven their capabilities of digging the personalised information by proactive filtering in many application domains and can also be a backbone platform in IoT for identifying personalised things, services and relevant artefacts by prevailing over information overload problems. This paper is a comprehensive literature review that categorises IoT recommender systems by exploring the literature’s different IoT based recommendation techniques. We conclude the paper by discussing the challenges and future scope for IoT based recommendations techniques to advancing and widening the frontiers of this research area. [ABSTRACT FROM AUTHOR]

• A novel confidence-aware recommender model (CARM) is proposed with representation learning. • The interactively of review is revealed to describe the interaction between user and item. • The robustness of the proposed model is verified by extensive experiments. The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request. [ABSTRACT FROM AUTHOR]

Recommender systems have been successfully applied to alleviate the problem of information overload and assist users' decision makings. Multi-stakeholder recommender systems produce the item recommendations to the end user by considering the perspective of multiple stakeholders. Existing research on multi-stakeholder recommendations relies on the offline evaluations only, and the online studies are still under investigation. This paper made the first attempt to examine the multi-stakeholder recommendations through online studies. On the one hand, we use online user studies to compare different recommendation models. On the other hand, we develop novel user interfaces to enhance the transparency and exploit the role of transparency in multi-stakeholder recommender systems. We collect our own dataset in educational learning and use it as case study to perform the online studies in multi-stakeholder recommendations. We observe that the explanation of the key parameters in the recommendation models can enhance the transparency, which further affects the decision making of different stakeholders. [ABSTRACT FROM AUTHOR]

The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems. • Most recommender systems use content-based filtering and predict recommendations with various machine learning algorithms. • Graph-based systems that employ various machine learning algorithms for recommendation are prevalent recently. • Several studies ignore personal attributes of users when producing recommendations. • Most systems use one source for their data, which is most often Allrecipes. • Offline evaluation with accuracy-based metrics is widely-used for measuring the performance of the FRS. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, social media has been widely used, and Internet information has been exploded, and consumers may experience information overload. Recommender systems using the social recommendation method that integrates social relationship information can provide users with target information that meets their needs. However, most of the existing methods only rely on the user's ordinary friends to make recommendations, neglecting another influential group, the opinion leaders. In this study, we propose a new social recommendation method based on opinion leaders. The proposed method assumes that the influence of the opinion leader on the user is much greater than that of the user's ordinary friends. The experimental results on two real datasets show that the proposed method not only has a better recommendation effect than the state-of-the-art recommendation algorithms, but also has a good performance in the cases of cold-start users. [ABSTRACT FROM AUTHOR]

Recommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender systems. The original Singular Value Decomposition (SVD) model is optimized by mining the co-occurrence and mutual exclusion of tags, and their features are constrained by the relationship between tags. Experiments on real dataset show that the proposed algorithm achieves superior performance to existing algorithms. [ABSTRACT FROM AUTHOR]

Social networks can provide massive amounts of information for communication among users and communities. The trust relationships in social networks can be utilized to reveal user preferences for improving the quality of social recommendation, which aims to mitigate information overload and provide users with the most attractive and relevant items or services. However, the data sparsity and cold-start issue degrade recommendation performance significantly. To address these issues, a novel trust-embedded collaborative deep generative model (TCDG) is proposed for exploiting multisource information (content, rating and trust) to predict ratings. TCDG employs deep generative model to unsupervisedly learn deep latent representations for item content through an inference network in latent space instead of observation space. Meanwhile, TCDG adopts probabilistic matrix factorization to map users into low-dimensional latent feature spaces by trust relationships, which can reflect users' mutual influence on the formation of users' opinions more accurately and learn better implicit relationships between items and users from content, rating and trust. In addition, an approach with an annealing parameter to calculate the maximum a posteriori estimates is proposed to learn model parameters. Experiments using four real-world datasets are conducted to evaluate the prediction and top-ranking performance of our model. The results indicate that TDCG has better accuracy and robustness than other methods for making recommendations. [ABSTRACT FROM AUTHOR]

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users' reviews to detect consumer opinions about the movie they have watched and to explore the individual's preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of 'data sparsity'. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods. [ABSTRACT FROM AUTHOR]

Summary: With the rapid proliferation of information and communication technology (ICT), the vast amount of available data creates information overload. The Websites and e‐commerce applications employ several information filtering methods such as personalized recommender system to manage the information overload. The recommender system assists the users in obtaining the desired list of products based on their interest. Several existing research works focus on the novelty or unexpectedness in the recommendation list while ensuring the quality to enhance the recommendation mechanism. It is essential to balance the unexpected and useful products or services to generate the satisfactory personalized recommendations with novelty. Thus, this paper proposes a novelty‐driven movie suggestion using integrated matrix factorization and temporal‐aware clustering optimization (NOMINATE). The proposed approach determines the personalized preferences through probabilistic matrix factorization (PMF) and contextually updates the rules and extracts the user preferences based on the inherent features of both the users and movies with temporal information. The NOMINATE approach also suggests the novelty‐driven, and desired top‐N movies to the users through the K‐means, and particle swarm optimization (PSO)‐based clustering algorithm with the help of LOD source. To identify the expert users, the NOMINATE approach applies the K‐means and PSO‐based clustering algorithm to enrich the personalized features of the users. Moreover, it integrates the relevant features with the preferred set of features for each user using the LOD source and decides a set of optimal preferences of the users. Finally, the NOMINATE approach generates the top‐N recommendation list for the corresponding user through ranking method. The experiment results stipulate that the NOMINATE approach personalize the top‐N movie recommendations with high performance regarding accuracy and novelty when compared with the existing recommendation method. [ABSTRACT FROM AUTHOR]

Recommender systems are tools and techniques to assist users in the content selection process thereby coping with the problem of information overload. For recommender systems, user authentication and feedback gathering are of crucial importance. However, the typical user authentication with username / password and feedback method with a star rating system are not user friendly and often bypassed. This article proposes an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics such as emotions. We studied the use case of video watching. Photos made with the front-facing camera of a tablet, smartphone, or smart TV are used as input of a facial recognition service. The persons in front of the screen can be identified. During video watching, implicit feedback for the video content is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An evaluation with a test panel showed that the recognized emotions are correlated with the user's star ratings and that happiness can be most accurately detected. So as the main contribution, this article indicates that emotion recognition might be used as an alternative feedback mechanism for recommender systems. [ABSTRACT FROM AUTHOR]

Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics. [ABSTRACT FROM AUTHOR]

In recent years, internet technologies and its rapid growth have created a paradigm of digital services. In this new digital world, users suffer due to the information overload problem and the recommender systems are widely used as a decision support tool to address this issue. Though recommender systems are proven personalization tool available, the need for the improvement of its recommendation ability and efficiency is high. Among various recommendation generation mechanisms available, collaborative filtering-based approaches are widely utilized to produce similarity-based recommendations. To improve the recommendation generation process of collaborative filtering approaches, clustering techniques are incorporated for grouping users. Though many traditional clustering mechanisms are employed for the users clustering in the existing works, utilization of bio-inspired clustering techniques needs to be explored for the generation of optimal recommendations. This article presents a new bio-inspired clustering ensemble through aggregating swarm intelligence and fuzzy clustering models for user-based collaborative filtering. The presented recommendation approaches have been evaluated on the real-world large-scale datasets of Yelp and TripAdvisor for recommendation accuracy and stability through standard evaluation metrics. The obtained results illustrate the advantageous performance of the proposed approach over its peer works of recent times. [ABSTRACT FROM AUTHOR]

Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems. [ABSTRACT FROM AUTHOR]

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field. [ABSTRACT FROM AUTHOR]

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Nowadays, people prefer to read news articles from online sources worldwide due to their easiness and availability. For the last few years, online searching for required information or content has been replaced by item recommendation, and news recommendation is also not an exception. For news recommendations, News Recommender System (NRS) helps the users to find the appropriate and pertinent content, alleviate the problem of information overload, and propose news that would be of interest to news readers. NRS also assists different users all around the world in this regard by recommending the most recent news articles based on their interests and past preferences. Many techniques such as traditional, Deep Learning (DL), and hybrid have been proposed to solve the NRS challenges and issues. DL techniques are considered one of the best techniques and have been successfully applied in various fields such as Natural Language Processing (NLP) and Computer Vision (CV). This survey article provides a detailed analysis of DL models-based techniques to build NRS. In this regard, firstly, a comprehensive comparison is provided between published survey articles on NRS and this research work. Secondly, it discusses the background of recommendation systems and their techniques. Furthermore, NRS is explored along with its current research challenges. Then background knowledge of DL and its methods have been discussed along with the analysis of year-wise published relevant articles having DL as the applied technique. The survey also presents widely used datasets and performance evaluation metrics used in the relevant literature. Finally, a detailed discussion provides several future directions and open research challenges for the researchers to consider DL applications in NRS. [ABSTRACT FROM AUTHOR]

Recommender systems are crucial in the big data era, effectively mitigating information overload. Existing recommendation methods are limited on highly sparse data and have mediocre recall performance. Group influence aggregates knowledge from different users or organizations to generate decisions, improving information fusion efficiency and group decision-making quality. In this paper, a group influence-based deep adversarial autoencoder (GI-AAE), is proposed for top-N recommendation. It leverages group influence to strengthen autoencoder latent features and address sparse data and uses adversarial learning from GANs to enhance reconstruction. The group influence based deep autoencoder (GI-AE) is the generative model for the GI-AAE. Experimental results show that the proposed algorithm is competitive and gets higher recall values. • A group influence-based deep adversarial autoencoder is proposed for recommendation. • Group influence increases the potential characteristics of the autoencoder. • Group influence helps alleviate the problem of sparse data sets. • The proposed algorithm gets better results, especially on recall values. [ABSTRACT FROM AUTHOR]

Recommender system is widely used in various fields for dealing with information overload effectively, and collaborative filtering plays a vital role in the system. However, recommender system suffers from its vulnerabilities by malicious attacks significantly, especially, shilling attacks because of the open nature of recommender system and the dependence on data. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of the existing methods of detecting shilling attack are based on user ratings, and one limitation is that they are likely to be interfered by obfuscation techniques. Moreover, traditional detection algorithms cannot handle different types of shilling attacks flexibly. In order to solve the problems, we proposed an outlier degree shilling attack detection algorithm by using dynamic feature selection. Considering the differences when users choose items, we combined rating-based indicators with user popularity, and utilized the information entropy to select detection indicators dynamically. Therefore, a variety of shilling attack models can be dealt with flexibility in this way. The experiments show that the proposed algorithm can achieve better detection performance and interference immunity. [ABSTRACT FROM AUTHOR]

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system's field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person's attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach. [ABSTRACT FROM AUTHOR]

Highlights • A fast recommendation approach is proposed. • The approach is based on self-adaptation and multi-thresholding. • An automatic convergence procedure is proposed to improve the performance of our approach. • The MAE is improved between 1.02% and 12.93%, and the computational time between 25.38% and 54.83%. Abstract Finding relevant and interesting items on e-commerce websites is a major challenge in the information era. Recommender systems are widely used to help users deal with the information overload problem by giving them personalized recommendations. Furthermore, they help businesses make more profits. Collaborative filtering represents one of the most successful recommendation approaches. In general, several existing recommendation methods have demonstrated good performance in terms of prediction quality. However, they may require prohibitive computational times, and they often confront the sparsity problem, which negatively affects the efficiency of the system. In this paper, we propose a fast recommendation algorithm based on self-adaptation and multi-thresholding. It is designed to deal with the mentioned drawbacks, and improve the prediction quality. In addition, it is able to converge automatically. Extensive experiments on two real-world data sets demonstrate that our proposed method can achieve significantly better performance than other state-of-the-art methods. Particularly, it improves the MAE between 1.02% and 12.93%, and the computational time between 25.38% and 54.83%. [ABSTRACT FROM AUTHOR]