Social Network-based Recommender Systems Research Articles

A security detection approach based on autonomy-oriented user sensor in social recommendation network

User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

Study on data sparsity in social network-based recommender system

With the development of information technology and the expanding of information resources, it is more difficult for people to get the information that they are really interested in, which is so-called information overload. Recommender systems are regarded as an important approach to deal with information overload, because it can predict users' preferences according to users' records. Matrix factorisation is very successful in recommender systems, but it faces the terrible problem of data sparsity. In this paper, the authors deal with the sparsity problem from the perspective of adding more kinds of information from social networks, such as friendships and tags into the recommending model in order to alleviate the sparsity problem. The paper also validates the impacts of users' friendships, tags and neighbours of items on reducing the sparseness of the data and improving the accuracy of recommending by the experiments using the dataset from real life.

Power users are not always powerful: The effect of social trust clusters in recommender systems

A recommender system (RS) is one that provides optimized information to users in an over-supply situation. The key to an RS is the accurate prediction of the behavior of the user. The matrix factorization (MF) method is used for this prediction in the early stages, and based on the recent development of social network service (SNS), social information is also utilized to improve the accuracy of prediction. In this paper, we use an RS internal trust cluster for the first time to further improve performance and analyze the characteristics of trust clusters. We propose a new approach, a trust-aware network (TAN) RS, to exploit these trust clusters. We also explore the impact and influence of power users in a social network-based RS using TAN RS, and analyze the impact of power users and clusters. From our experiments, we find that the use of TAN RS can enhance the prediction accuracy of RSs; we also show that power users and the sizes of clusters are not significant, and that normal users and ordinary sizes of clusters contribute to a reduction in prediction errors in social RSs.

Toward a rapid development of social network-based recommender systems

Actions carried out in social networks such as Facebook, Twitter, Foursquare, and the like, can greatly benefit the quality of recommendations provided by recommender systems. Usual interactions on these platforms provide valuable information on user preferences and proximity with other users. However, the use of this information by external recommender systems is still incipient and developers have little support to do it efficiently. In this paper, we introduce a model-driven development framework for recommenders systems based on social networks. The core of this framework is an abstract, social network-independent model of recommender systems, which combines domain-independent concepts of collaborative filtering with basic concepts of social networks that can be exploited for recommendation purposes. From this model, developers can specify the structure and algorithms of domain-specific recommender systems at a high abstraction level. An automatic code generation strategy supports the implementation phase. Experiments show promising results in development-time saving.

Study on data sparsity in social network-based recommender system

With the development of information technology and the expanding of information resources, it is more difficult for people to get the information that they are really interested in, which is so-called information overload. Recommender systems are regarded as an important approach to deal with information overload, because it can predict users' preferences according to users' records. Matrix factorisation is very successful in recommender systems, but it faces the terrible problem of data sparsity. In this paper, the authors deal with the sparsity problem from the perspective of adding more kinds of information from social networks, such as friendships and tags into the recommending model in order to alleviate the sparsity problem. The paper also validates the impacts of users' friendships, tags and neighbours of items on reducing the sparseness of the data and improving the accuracy of recommending by the experiments using the dataset from real life.

Multirelational Social Recommendations via Multigraph Ranking.

Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.

Mining Implicit Correlations between Users with the Same Role for Trust-Aware Recommendation

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.