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User identification is an essential technical support for downstream tasks such as recommendation systems, information retrieval, and collaborative filtering. Computing the similarity between user display names through classifiers is an effective solution for user identification across social networks. However, there are two problems with existing methods. Applying expert domain knowledge to extract handcrafted features of display names ignores the semantic information, resulting in poor performance of these methods. Selecting influential display names, and handcrafted features in user identification problems are also one of the difficulties. To solve these two problems, we propose a method based on the multi-feature fusion of display names using gated units. First, we extract the deep semantic features of display names through the BERT pre-trained multi-language model. Then, the gated mechanism is applied to select the handcrafted features we extracted to retain the essential features. Then, the adaptive factors are used to fuse handcrafted features and deep features to obtain user identification results across social networks. Finally, the efficiency of our model is verified on three constructed real-world multilingual display names datasets across multiple online social networks and compared with existing state-of-the-art methods. Experimental results show that the proposed algorithm outperforms the compared algorithms. [ABSTRACT FROM AUTHOR]

In order to meet the protection needs of user privacy data in social networks, this paper proposes a computer intelligent collaborative filtering algorithm for personalized network data recommendation systems. This algorithm predicts user preferences for specific items by utilizing user evaluation information on groups of similar feature items, thereby achieving personalized recommendations. The experimental results show that as the number of project feature selections N increases, the MAE, RMSE, and NDCG5 of the algorithm gradually improve. This is mainly attributed to increasing the number of features under a fixed similarity threshold, which makes the data granularity finer and helps to describe project features more accurately. In the case of a fixed number of project feature selections N, the impact of the number of nearest neighbors s in similar groups on algorithm performance was further studied. The results showed that with the increase of s, MAE, RMSE, and NDCG5 showed a decreasing trend. Although the algorithm suffers from certain losses in recommendation accuracy, it is still within an acceptable range. It is worth noting that due to the system only using generalized data as input, user privacy data is effectively protected. Based on the comprehensive experimental results, this algorithm has significant value in practical applications. [ABSTRACT FROM AUTHOR]

In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

Recently, there has been a surge in the popularity of online groups on event-based social networks (EBSNs) like Meetup and Douban Event. These groups cater to individuals who share common interests, provide comments, and engage in various activities. Our research focuses on online group recommendations, based on which users can conveniently join groups and participate in offline events organized by the groups. Traditional group recommendation methods do not work well in addressing this problem because they lack the ability to deal with the challenges posed by dynamic user interests, sparse supervision signals, and heterogeneous networks simultaneously. The self-attention model with contrastive learning for online group recommendation (SCL4GR) presented in this study exploits user-group sequential data, online and offline networks in a unified framework to predict user preferences for groups. First, a graph encoder is used to capture the high-order social interaction between users. Then, the pattern of dynamic interests is captured by sequence model Transformer. Furthermore, the contrastive learning is employed to derive self-supervision signals from both online and offline networks. We conduct experiments on three real-world datasets. Experimental results show that our SCL4GR consistently outperforms state-of-the-art methods for online group recommendation in EBSNs. [ABSTRACT FROM AUTHOR]

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

Today, due to the expansion of the Internet and social networks, people are faced with a vast amount of dynamic information. To mitigate the issue of information overload, recommender systems have become pivotal by analyzing users' activity histories to discern their interests and preferences. However, most available social image recommender systems utilize a static strategy, meaning they do not adapt to changes in user preferences. To overcome this challenge, our paper introduces a dynamic image recommender system that leverages a deep reinforcement learning (DRL) framework, enriched with a novel set of features including emotion, style, and personality. These features, uncommon in existing systems, are instrumental in crafting a user's characteristic vector, offering a personalized recommendation experience. Additionally, we overcome the challenge of state representation definition in reinforcement learning by introducing a new state representation. The experimental results show that our proposed method, compared to some related works, significantly improves Recall@k and Precision@k by approximately 7%–10% (for the top 100 images recommended) for personalized image recommendation. [ABSTRACT FROM AUTHOR]

Wayfinding, which is a part of learning in connectivist learning, involves consolidating a wide variety of resources and information and building connections among them. However, learners often encounter difficulties in wayfinding, and are lost without technological support in connectivist learning. This study examined the wayfinding processes occurring within a network of learners in a personal social knowledge network (PSKN), explored differences in behavior patterns between high and low performers in PSKN. The results reveal the diversity and complexity of wayfinding in a PSKN, including finding and connecting nodes, forming cognitive maps, finding and filtering information, and creating new nodes. Moreover, the characteristics of wayfinding in the PSKN differed across participants, and high- and low-performing participants demonstrated different and unique wayfinding behavioral patterns, which provided a basis for comprehensive analyses of wayfinding. These findings can be used to provide instructional support and network navigation in connectivist learning for learners at various performance levels. The proposed PSKN shows promise in facilitate wayfinding including finding nodes and connecting nodes, as well as relations between knowledge nodes and the course base demonstrated by PSKN, providing great convenience for learners to form cognitive maps based on the node sequence. Compared with current studies, this research focuses on diversified interaction data and resource behavior rather than teaching videos and quizzes or exercises as the main resources and considering that course and technological factors influence the ways in which learners access resources in connectivist learning. [ABSTRACT FROM AUTHOR]

Community detection in social networks is of great importance and is used in a variety of applications such as recommendation systems and targeted advertising. While detecting dense groups with high levels of connectivity and similar interests between their members is the main target of traditional network analysis, finding network members with quite different behavior than the majority of nodes is important as well. These nodes are known as outliers, and their accurate detection can be very useful; when outliers are marked as noisy nodes, their early exclusion from analysis can lead to high computational profits. On the other hand, they can represent interesting components that call for further investigation to find the reasons for their outlying behavior and possible ways to include them in a neighboring community. Both community and outlier detection are challenging in temporal environments where changes occur in real time; thus, dynamic methods need to be deployed rather than to static methods. In our work, we take into account the content of the network, in contrast to most of related studies, where only the network's structure contributes to community formation. We define an adaptive outlier score to be assigned to each node in order to quantify its outlierness, and introduce a complete online community detection algorithm that analyzes both the network's structure and content while at the same time detecting community outliers. To evaluate our method, we retrieved and processed two real datasets regarding social networks with temporal and content information. Experimental results show that our method is capable of detecting outliers in real-time evolving communities and provides an outlier score which is a better metric of each node's outlierness compared to widely used metrics. Finally, experimental results indicate that our method is suitable for predicting the status of future nodes based on their current outlier score. [ABSTRACT FROM AUTHOR]

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences. [ABSTRACT FROM AUTHOR]

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Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit's unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining. [ABSTRACT FROM AUTHOR]

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids' selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm's usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines. [ABSTRACT FROM AUTHOR]

With the development of Location-Based Social Networks, successive Point Of Interest (POI) recommendation systems have become a hot spot in the field of recommendation systems. Successive POI recommendation systems suggest to users new and interesting places to visit. However, in real-life POI recommendation, there are often a small number of users facing a huge number of POIs. Traditional successive POI recommendation methods are not capable to deal with the large sparse datasets, as they only consider the simple relationship between users and POIs. They do not use the context information of users and POIs, which can enable better recommendation results. To utilize the context information, this paper proposes a novel successive POI recommendation method, SQPMF, which integrates user personal preferences, user social relationships and POI transition relationships into the system for accurate recommendation of the next POI. Our experimental evaluation using three real-life datasets, Gowalla, Foursquare and Brightkite, shows that our method SQPMF consistently outperforms all state-of-the-art methods in recommendation of successive POIs. Compared with other methods, SQPMF improves Precision, Recall and F1-score by an average of at least 6.1 % , 5.8 % and 5.7 % respectively on three publicly available datasets. [ABSTRACT FROM AUTHOR]

Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user's social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user's closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%. [ABSTRACT FROM AUTHOR]

Objective: The overall goal of this work is to produce a set of recommendations (SoNHR–Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. Methods: This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. Results: The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. Conclusions: These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas. [ABSTRACT FROM AUTHOR]

Session recommendation is a technology that pro)cesses data referring to the user-anonymous session behavior analysis. This technique has a wide range of applications in e-commerce, videos, social networks, and other fields and it is a hot topic in current recommendation system research. Multi-hypergraph neural network with fusion of location information for session-based recommendation (MHGNN-LI) captures multi-dimensional interest features in the session through the multi-hypergraph representation and combines different contrastive learning methods to alleviate the problem of data sparsity. It also considers the dependence order of the recommended items to improve the recommendation effect. Concerning the methodology of work, the model first constructs a session hypergraph to obtain the interest features of multiple items in the session. Secondly, it decouples the interest features into global and local features within the session, and then it integrates the item features with location information into the local interest features. Finally, it introduces a line graph at the feature level between the different sessions and maximizes the mutual information between sessions and within them, through contrastive learning, to enhance the expression ability of session data. As for the validation, comparative experiments, with related models on two public datasets, Tmall and Diginetica, show that the MHGNN-LI model has improved on P@10 and MRR@10 by, at least, 15.47% and 20.51%, as well as 9.77% and 12.37% on the Tmall and Diginetica datasets, respectively. [ABSTRACT FROM AUTHOR]

Motivated by real-world applications such as recommendation systems and social networks where only "likes" or "friendships" are observed, we consider a challenging multi-label learning problem where the observed label consists only of positive and unlabeled entries and the feature matrix contains missing entries. The problem is an enhanced instance of PU (positive-unlabeled) learning. Due to highly incomplete data, traditional multi-label learning algorithms are not directly available in such a scenario. In this paper, we propose a bi-directional matrix completion approach that exploits the matrix low-rank property to recover missing feature entries and label entries. Specifically, we introduce a low-rank co-embedding framework that integrates a low-rank matrix complete model and prediction model to jointly recover missing entries. In our framework, the prediction model can be conducted efficiently by dividing the low-rank matrix into a part capturing feature information and a part capturing information outside the feature space. Furthermore, we incorporate label embedding and graph regularized embedding together to improve matrix completion performance, which not only takes feature graph-structured information into account but also simultaneously exploits label semantic structured information. We provide an efficient alternative minimization scheme to solve the proposed problem. The experiments on transductive and inductive incomplete multi-label learning demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users' next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial–temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users' check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26–6.55 % and MAP@k by 3.49–6.55 % . Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09 % on the Hit@k. The code can be downloaded at https://github.com/Rih0/sstp. [ABSTRACT FROM AUTHOR]

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today's RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers. [ABSTRACT FROM AUTHOR]

Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre‐defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self‐similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurst‐based Diffusion Model (TSC‐HDM), which calculates H based on the stationary or non‐stationary characteristic of the time series. TSC‐HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H > 0.5. The proposed model has been tested on four real‐world OSN datasets. The results have been compared against four other IM models – Independent Cascade, Weighted Cascade, Trivalency, and Hurst‐based Influence Maximization. TSC‐HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC‐HDM has attained 344% better average influence spread than other state‐of‐the‐art models namely LIR, A‐Greedy, LPIMA, Genetic Algorithm with Dynamic Probabilities, NeighborsRemove, DegreeDecrease, IGIM, IRR, and PHG. [ABSTRACT FROM AUTHOR]

Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms. [ABSTRACT FROM AUTHOR]

Abstract Motivation Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As such, similarly to the job market, it is important to ensure the success and income (usually related to the follower counts) of CCs reflect the quality of their work. Since quality cannot be observed directly, two other factors govern the network-formation process: (a) the visibility of CCs (resulted from, e.g., recommender systems and moderation processes) and (b) the decision-making process of seekers (i.e., of users focused on finding CCs). Prior virtual experiments and empirical work seem contradictory regarding fairness: While the first suggests the expected number of followers of CCs reflects their quality, the second says that quality does not perfectly predict success. Results Our paper extends prior models in order to bridge this gap between theoretical and empirical work. We (a) define a parameterized recommendation process which allocates visibility based on popularity biases, (b) define two metrics of individual fairness (ex-ante and ex-post), and (c) define a metric for seeker satisfaction. Through an analytical approach we show our process is an absorbing Markov Chain where exploring only the most popular CCs leads to lower expected times to absorption but higher chances of unfairness for CCs. While increasing the exploration helps, doing so only guarantees fair outcomes for the highest (and lowest) quality CC. Simulations revealed that CCs and seekers prefer different algorithmic designs: CCs generally have higher chances of fairness with anti-popularity biased recommendation processes, while seekers are more satisfied with popularity-biased recommendations. Altogether, our results suggest that while the exploration of low-popularity CCs is needed to improve fairness, platforms might not have the incentive to do so and such interventions do not entirely prevent unfair outcomes.

With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic. [ABSTRACT FROM AUTHOR]

With the massive quantification of networked data and the development of geo-social networks (GSNs), group activities are prevalent in people's life. The objects of recommendation systems are extended from individuals to user groups. Point-of-interest (POI) group recommendation problem is also gradually known as a hot research topic. However, the traditional methods are not suitable for group recommendation in geographic social networks, due to the multifactorial influence of user preferences in GSNs and the complexity of the group decisionmaking process. To reveal user preferences and the effect of the group decision process on group recommendation, this paper proposes a neural network-based model for dynamic POI group recommendation by leveraging multidimensional user preference. Firstly, the proposed model combines temporal and spatial factors to calculate user preferences based on user behavior activity records and builds a group-point-of-interest perception graph with group as unit. Next, this paper adds the influence of collaborative users to model group preferences, which fully considers the characteristics of GSNs, to ensure the accuracy of POI group recommendation. Finally, a neural network-based model can be constructed to simulate group decision-making, which can ensure the accuracy of POI recommendations. This paper conducts extensive experiments by comparing the existing group recommendation algorithms on the real datasets to demonstrate the performance of the method proposed in this paper. Experimental results show that the proposed method is significantly better than the existing algorithms in terms of the hit rate of POI, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Link prediction on knowledge graphs has become increasingly important in various fields, including recommender systems, question answering, and social networks. To address this challenge, ConvKB, a state-of-the-art model in deep learning approaches, has been proposed. However, ConvKB is limited in its ability to exploit general information about relations because it uses the same filters for all relations. Additionally, ConvKB's design, consisting of a convolution layer and a linear layer, may not provide enough parameters to store necessary information during feature learning. To address these limitations, this paper proposes the ConvHyper model, which combines the ConvKB model with a HyperNetwork architecture to create relation-specific weights for convolutional neural network layers. Specifically, the embedding of relations is linearly combined through a HyperNetwork to generate weights for the base neural network. This HyperNetwork architecture helps reduce the complexity of the search space to identify optimal weights and create a relation-specific weight structure for neural network layers. Experimental results show that ConvHyper significantly improves on all metrics in four well-known datasets. ConvHyper improves on H@1 up to 5.5% and achieves better results on other metrics, ranging from 0.8% to 1%. We also compared ConvHyper with other CNN-based models and found that many metrics have better values, particularly from 1.1% to 4.7% on the H@10 metric. Furthermore, we analyzed the influence of hyperparameters and found that the learning rate and negative samples significantly affect the model's results. [ABSTRACT FROM AUTHOR]

Advertising has been one of the most effective and valuable marketing tools for many years. Since companies spend vast amounts of money on product advertising, they put their best effort into making the advertising effective, consequently increasing product sales. Advertising recommender systems can significantly help make posted ads more effective. Due to the incredible popularity of social networks in the last two decades and the massive amount of user's activity on these networks, a great opportunity has been provided to elicit user data and post product promotions. Recommender systems for social networks can use various data to make recommendations, including content, post features, individuals, and even context-based information (e.g., activity, location, time, individual, and relational data). One of the upcoming challenges is the study of feature interactions and their valuation. The critical question is: "Can this data, especially contextual data, lead to improved advertising recommendations?" Considering the instantaneous growth of social network data, it is also imperative to verify scalability based on time criteria. Since earlier studies have investigated the influence of a limited number of contextual features on the recommender system, it is necessary to conduct a study that comprehensively utilizes the diverse contextual data available in social networks. Therefore, we use a hybrid context-based SOM-SPM approach to meet the challenges raised. Our method improves the accuracy of offered product's advertisements for target users. The proposed method improves the performance of the advertising recommender system by constituting a post-context feature matrix based on context dimensions and SPM algorithms to learn behavioral patterns. We experiment with this method on two datasets and evaluate the results with MSE, RMSE, MAE metrics and Davies-Bouldin Index. The results indicate that our method outperforms algorithms like FM, SBS, AFM and MF-LOD. The experiment results also indicated the time scalability of the proposed approach. We also analyzed the influence of various context combinations on recommendation accuracy and then calculated each context-to-context feature interaction to better understand each context's ad recommendation efficiency. For example, the feature interaction matrix analysis in the Instagram dataset revealed that features related to the broadcast time of posts and users are more critical than other contextual features. Therefore, companies can plan more accurately on the time and user's behavioral attributes to achieve advertising effectiveness and minimize spam ads. [ABSTRACT FROM AUTHOR]

Recently, social networks have been regarded as auxiliary information to mitigate the data sparsity issue in recommender systems. However, most existing social recommendation methods fail to effectively capture the relations between multiple behaviors, resulting in the correlated behaviors being unable to make semantic complements to the target behavior and sparse behavior data features. To alleviate the above problems, we propose a novel method based on graph neural network, namely Multiple Behavior Interactive Enhanced Social-aware Recommendation (MISR), which can dynamically acquire more fine-grained relations and differences between different behaviors and combine features of temporal sequences to capture potential interactions. In addition, we develop a global enhanced module to fully learn the enhanced user representation, empowering MISR to capture jointly the heterogeneous strengths of global social context and social relations. Extensive experiments on three real-world recommendation datasets validate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recommender systems utilize social relationships to improve recommendation performance. This study explores social characteristics and how they affect recommendation performance. We define social characteristics as network embeddedness and preference heterogeneity. Taking rating characteristics as control variables, we build a regression model to explore the impact of two social characteristics on user-level predictive accuracy and the moderating effect of preference heterogeneity on the relationship between network embeddedness and user-level predictive accuracy. The results suggest that network embeddedness positively influences predictive accuracy, whereas preference heterogeneity negatively influences it. Our research reveals that as the preference heterogeneity increases, the positive effect of network embeddedness on predictive accuracy weakens. Preference heterogeneity has a greater impact on user-level predictive accuracy than network embeddedness. Our findings provide management implications for recommender system designers, which is of great significance for improving the accuracy of user-level prediction and reducing user complaints. [ABSTRACT FROM AUTHOR]

Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers' activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizations. [ABSTRACT FROM AUTHOR]

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user's higher-order social friends and community neighbors on the user is obtained according to the user's higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI's higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Recommender systems (RSs) have become an inseparable part of our everyday lives. They help us find our favorite items to purchase, our friends on social networks, and our favoritemovies towatch. Traditionally, the recommendation problem was considered to be a classification or prediction problem, but it is now widely agreed that formulating it as a sequential decision problem can better reflect the user-system interaction. Therefore, it can be formulated as a Markov decision process (MDP) and be solved by reinforcement learning (RL) algorithms. Unlike traditional recommendation methods, including collaborative filtering and content-based filtering, RL is able to handle the sequential, dynamic user-system interaction and to take into account the long-term user engagement. Although the idea of using RL for recommendation is not new and has been around for about two decades, it was not very practical, mainly because of scalability problems of traditional RL algorithms. However, a new trend has emerged in the field since the introduction of deep reinforcement learning (DRL), which made it possible to apply RL to the recommendation problem with large state and action spaces. In this paper, a survey on reinforcement learning based recommender systems (RLRSs) is presented. Our aim is to present an outlook on the field and to provide the reader with a fairly complete knowledge of key concepts of the field. We first recognize and illustrate that RLRSs can be generally classified into RL- and DRL-based methods. Then, we propose an RLRS framework with four components, i.e., state representation, policy optimization, reward formulation, and environment building, and survey RLRS algorithms accordingly. We highlight emerging topics and depict important trends using various graphs and tables. Finally, we discuss important aspects and challenges that can be addressed in the future. [ABSTRACT FROM AUTHOR]

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

Social recommendation systems leverage the abundant social information of users existing in the current Internet to mitigate the problem of data sparsity, ultimately enhancing recommendation performance. However, most existing recommendation systems that introduce social information ignore the negative messages passed by high-order neighbor nodes and aggregate messages without filtering, which results in a decline in the performance of the recommendation system. Considering this problem, we propose a novel social recommendation model based on graph neural networks (GNNs) called the preference-aware light graph convolutional network (PLGCN), which contains a subgraph construction module using unsupervised learning to classify users according to their embeddings and then assign users with similar preferences to a subgraph to filter useless or even negative messages from users with different preferences to attain even better recommendation performance. We also designed a feature aggregation module to better combine user embeddings with social and interaction information. In addition, we employ a lightweight GNN framework to aggregate messages from neighbors, removing nonlinear activation and feature transformation operations to alleviate the overfitting problem. Finally, we carried out comprehensive experiments using two publicly available datasets, and the results indicate that PLGCN outperforms the current state-of-the-art (SOTA) method, especially in dealing with the problem of cold start. The proposed model has the potential for practical applications in online recommendation systems, such as e-commerce, social media, and content recommendation. [ABSTRACT FROM AUTHOR]

Graph Neural Networks have been widely used in social recommendation systems. However, with the increase of graph nodes and diffusion depth, they tend to suffer from graph sparsity and over-smoothing, which inhibit their performance. In this work, we propose the multi-relational attention network, named as MRAN, for social recommendation. Our model has three distinctive characteristics: (i) it alleviates the data sparsity problem in social recommendation scenarios by incorporating both user social relations and item homogeneous relations as supplementary information; (ii) it mimics the structure of influence diffusion in user and item domain via an iteratively aggregating structure; (iii) it has a two-level attention mechanism at the diffusion and aggregating level, enabling it to differentiate importance of embeddings to overcome the over-smoothing problem. Experiments conducted on two large-scale representative datasets demonstrate that the proposed model outperforms previous methods substantially. The ablation study shows that the performance of MRAN can be further improved avoid over-smoothing by increasing the diffusion depth. [ABSTRACT FROM AUTHOR]

Recommender systems play a significant role in helping online users to find the relevant items based on their past preferences. With the sweep of the social network, the social recommendation has emerged that relies on users' social connections to make personalized recommendations of items. A social recommender system leverages users' social connections to address sparsity in rating data and improve recommendation efficiency. Despite their efficacy, the risk of privacy breach is high in the social recommender system as it uses sensitive information for the recommendation. To address this issue, the paper proposes a novel privacy-preserving matrix factorization-based network embedding model that employs an objective perturbation mechanism. The intrinsically nonlinear characteristics of users' social relationships are learned via a Deep Neural Network. The proposed framework uses the differential privacy to secure users' social ties. The recommendation is made by considering the users' personal preferences and social influence generated by the differential private network embedding model. The experimental evaluation of benchmark datasets proves that the proposed method can secure users' privacy without degrading the recommendation accuracy. [ABSTRACT FROM AUTHOR]

The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users' behavioral patterns and improve the accuracy of location-based services, point-of-interest (POI) recommendation has become an important task. In contrast to the general task of product recommendation, POI recommendation faces the problems of the sparsity and weak semantics of user check-in data. To address these issues, an increasing number of studies have improved the accuracy of POI recommendations by introducing contextual information such as geographical, temporal, textual, and social relations. However, the rich context also brings great challenges to POI recommendation, such as the low utilization rate of context information, difficulty in balancing the richness of contextual information, and the complexity of the recommendation matrix. Considering that similar users have more interest preferences in common than users generally have, the check-in information of similar users has greater reference meaning. Thus, we propose a personalized POI recommendation method named CULT-TF, which incorporates similar users' contextual information into the tensor factorization model. First, we present a user activity model and a user similarity model, which integrate contextual information to calculate the user activity and similarity between users. According to user activity, the most representative active users are selected as user clustering centers, and then users are clustered based on user similarity into several similar user clusters (C). Next, we construct a third-order tensor (user-location-time matrix) for each user cluster by using user activity, POI popularity, and time slot popularity as the eigenvalues in the user (U), location (L), and time (T) dimensions, and the eigenvalue of each dimension is modeled by integrating contextual information of users' check-in behavior at the user, location, and time levels. Similar user clustering reduces the number of users in tensor modeling, reducing the U dimension. To further reduce the complexity of the recommendation matrix, the reduction of the L dimension is achieved through ROI (region of interest) clustering, and the reduction of the T dimension is achieved through time slot encoding. Then, we use tensor factorization (TF) to obtain the recommendation results. Our method decreases the complexity of the tensor matrix and integrates rich contextual information on users' check-in behavior. Finally, we conducted a comprehensive performance evaluation of CULT-TF using real-world LBSN datasets from Brightkite. The experimental results show that our proposed method performs much better than other recommendation methods in terms of precision and recall. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern's friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation. [ABSTRACT FROM AUTHOR]

Traditional methods have resulted in lower-quality search results with a lower accuracy rate. This problem is addressed and a recommended technique using deep learning methods is provided with the goal of improving prediction quality. Via this paper, a novel paradigm for pertinent product recommendations in social networks is provided. The major goal of this strategy is to let computers learn automatically without any assistance from humans, consequently controlling operations as needed. The social input data set in this proposed study is first pre-processed to remove noise. Following that, a Fisher discriminant method based on information is used for feature extraction. Then, using the Hierarchical Agglomerative and Attribute-based Clustering procedure, the features are chosen from the retrieved ones. Following that, such clusters are predicted using the stacked DenseNet121 method, and Attention-based MLP is used to propose the product. Finally, to verify the effectiveness of the suggested system, the expected output was evaluated, the performance measure was examined, and comparisons with conventional methods were made. Out of 2033 reviews, the suggested approach has a positive score percentage of 92.22%. The investigation demonstrates that the suggested system is more effective at providing improved results for pertinent product recommendations. [ABSTRACT FROM AUTHOR]

This paper proposes a sequential rules-based recommendation system, called STS-Rec. It addresses the main drawbacks of sequential patterns mining approaches for POI (Point of interest) recommendation by considering both temporal and social influences to perform short-term recommendations. STS-Rec first transforms mobility data into location sequences. Then, it incrementally mines sequential recommendation rules in these sequences. In contrast with standard sequential recommenders, the proposal (1) discovers rules that tolerate locations' order variations by loosening the strict ordering constraint of location sequences, (2) builds a tree-based model to incrementally mine recommendation rules, and (3) supports short and long-term POI recommendation by using a user-defined window by extracting patterns that appear within a maximum number of consecutive locations. To take the temporal influence into account, STS-Rec adapts its mining strategy to include the temporal context in location data. Hence, the conventional rule mining problem is redefined to mine time-extended recommendation rules. An experimental evaluation conducted on two large-scale real check-in datasets from Gowalla and Brightkite shows that the proposed model outperforms two state-of-the-art sequential models in terms of accuracy and coverage. [ABSTRACT FROM AUTHOR]

Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain. [ABSTRACT FROM AUTHOR]

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms. [ABSTRACT FROM AUTHOR]

Social recommendation systems can improve recommendation quality in cases of sparse user–item interaction data, which has attracted the industry's attention. In reality, social recommendation systems mostly mine real user preferences from social networks. However, trust relationships in social networks are complex and it is difficult to extract valuable user preference information, which worsens recommendation performance. To address this problem, this paper proposes a social recommendation algorithm based on multi-graph contrastive learning. To ensure the reliability of user preferences, the algorithm builds multiple enhanced user relationship views of the user's social network and encodes multi-view high-order relationship learning node representations using graph and hypergraph convolutional networks. Considering the effect of the long-tail phenomenon, graph-augmentation-free self-supervised learning is used as an auxiliary task to contrastively enhance node representations by adding uniform noise to each layer of encoder embeddings. Three open datasets were used to evaluate the algorithm, and it was compared to well-known recommendation systems. The experimental studies demonstrated the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

With the development of mobile networks and the rapid prevalence of location‐based social networks (LBSN), a massive volume of spatiotemporal data has been generated, which is valuable for points of interest (POI) recommendation. However, current studies have not unleashed the full power of such spatiotemporal data, which either explore only a single dimension of the data or consider multiple factors in an asynchronous fashion. In this article, we propose a novel spatiotemporal network‐based recommender framework (STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensive conceptual model of spatiotemporal data, involving various essential factors for POIs recommendation. On top of the conceptual model, we design a series of meaningful meta‐paths that simultaneously consider the time and location factors to precisely capture the semantics of user behaviours. By profiling users based on their embedded meta‐paths, our approach can yield meaningful POIs recommendations. We have evaluated our proposal using a realistic dataset obtained from Foursquare and Gowalla, the results of which show that our STNBR model outperforms existing approaches. [ABSTRACT FROM AUTHOR]

Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things. [ABSTRACT FROM AUTHOR]

A news-recommendation system is designed to deal with massive amounts of news and provide personalized recommendations for users. Accurately modeling of news and users is the key to news recommendation. Researchers usually use auxiliary information such as social networks or item attributes to learn about news and user representation. However, existing recommendation systems neglect to explore the rich topics in the news. This paper considered the knowledge graph as the source of side information. Meanwhile, we used user topic preferences to improve recommendation performance. We proposed a new framework called NRTEH that was based on topic and entity preferences in user historical behavior. The core of our approach was the news encoder and the user encoder. Two encoders in NRTEH handled news titles from two perspectives to obtain news and user representation embedding: (1) extracting explicit and latent topic features from news and mining user preferences for them; and (2) extracting entities and propagating users' potential preferences in the knowledge graph. Experiments on a real-world dataset validated the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Recommending Smart Social Network (SSN) items which are in line with user preferences is one of the significant applications in SSNs such as Event-Based Social Networks (EBSNs) and Smart object Based Social Network (SBSN). It is well acknowledged that geographical and social contextual influences play an important role in SSN item Recommender System (RS). Whereas incorporating such contextual influences into SSN item RSs is a challenging issue which needs to be addressed along with the conventional RS challenges (cold-start problem and data sparsity). To this end, a novel SSN item recommendation architecture (named as 3T-IEC*) based on Graph Convolutional Network (GCN) is proposed. Specifically, 3T-IEC* constructs an SSN item location graph based on physical distance between two SSN items and leverages a GCN-based representation learning method to capture geographical influence of SSN items; which not only incorporates the geographical preferences but also delivers a convincing idea to solve the data sparsity problem. Sequentially, 3T-IEC* deploys one more GCN to incorporate users' social associations. Furthermore, 3T-IEC* leverages self-attention technique with multiple heads to incorporate user's preferences over SSN items' multiple features. The experimental results on four real life datasets demonstrate superior performance of 3T-IEC* compared with state-of-the-art methods such as SkyLine (149%), 3T-IEC (54%), DeepWalk (38%), SORec (9%), and NGCF (7%). [ABSTRACT FROM AUTHOR]

Recommendation system utilizes user-item interactions and information on user's attributes to infer the user's interests and use them to make recommendations for the user. Graph neural network(GNN) has become more widely used in recommendation systems in recent years, because of their ability to naturally integrate node information and topology. However, most of the current recommendation methods based on graph structure only focus on a single recommendation domain (for example,session-based recommendation models or social recommendation models), without taking into account both the user's behavior information and the user's social relationships; Furthermore, session-based recommendation models usually use a recurrent neural network (RNN) to model user session, while RNN only focuses on the short-term impact of sessions and cannot cover all the information of sessions. Therefore, this paper proposes a novel session-based social recommendation model called GNNRec, which first utilizes gated graph neural network (GGNN) to represent users' session information, and then uses graph attention network (GAT) to aggregate social information of users and friends on social networks to effectively model users' interests. In this paper, experiments are conducted on two large datasets——Douban and Epinions, and the results show that the GNNRec model performs significantly better than current mainstream recommendation models. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system. [ABSTRACT FROM AUTHOR]

The graph neural network has received significant attention in recent years because of its unique role in mining graph-structure data and its ubiquitous application in various fields, such as social networking and recommendation systems. Although most work focuses on learning low-dimensional node representation in static graphs, the dynamic nature of real-world networks makes temporal graphs more practical and significant. In this paper, we propose a dynamic graph representation learning method based on a temporal graph transformer (TGT), which can efficiently preserve high-order information and temporally evolve structural properties by incorporating an update module, an aggregation module, and a propagation module in a single model. The experimental results on three real-world networks demonstrate that the TGT outperforms state-of-the-art baselines for dynamic link prediction and edge classification tasks in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user's textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system. [ABSTRACT FROM AUTHOR]