Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social information to help mitigate the data sparsity and cold start problems, and it has been demonstrated that the social-based recommendation algorithms can efficiently improve the recommendation performance. However, few of the existing algorithms have considered using multiple types of relations within one social network. In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a Social Collaborative Filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilize multiple types of relations in a heterogeneous social network. In addition, Hete-CF is a general approach and can be used in arbitrary social networks, including event based social networks, location based social networks, and any other types of heterogeneous information networks associated with social information. The experimental results on two real-world data sets, DBLP (a typical heterogeneous information network) and Meetup (a typical event based social network) show the effectiveness and efficiency of our algorithm

A Distributed Method for Trust-Aware Recommendation in Social Networks

This paper contains the details of a distributed trust-aware recommendation system. Trust-base recommenders have received a lot of attention recently. The main aim of trust-based recommendation is to deal the problems in traditional Collaborative Filtering recommenders. These problems include cold start users, vulnerability to attacks, etc.. Our proposed method is a distributed approach and can be easily deployed on social networks or real life networks such as sensor networks or peer to peer networks

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective

Understanding mechanisms driving link formation in dynamic social networks is a long-standing problem that has implications to understanding social structure as well as link prediction and recommendation. Social networks exhibit a high degree of transitivity, which explains the successes of common neighbor-based methods for link prediction. In this paper, we examine mechanisms behind link formation from the perspective of an ego node. We introduce the notion of personalized degree for each neighbor node of the ego, which is the number of other neighbors a particular neighbor is connected to. From empirical analyses on four on-line social network datasets, we find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. This is complementary to the finding of Adamic and Adar that neighbor nodes with higher (global) degree are less likely to lead to new link formations. Furthermore, on directed networks, we find that personalized out-degree has a stronger effect on link formation than personalized in-degree, whereas global in-degree has a stronger effect than global out-degree. We validate our empirical findings through several link recommendation experiments and observe that incorporating both personalized and global degree into link recommendation greatly improves accuracy.Comment: To appear at the 10th International Workshop on Modeling Social Media co-located with the Web Conference 201