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 utilise multiple types of relations in a heterogeneous social network. More importantly, 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 a real-world dataset DBLP (a typical heterogeneous information network) demonstrate the effectiveness of our algorithm.
Luo et al. REF proposed a collaborative filtering-based social recommendation method, called HeteCF, using heterogeneous relations.
Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations
2014 IEEE International Conference on Data Mining
2014 IEEE International Conference on Data Mining
Computer Science