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

Chen Luo, Wei Pang, Zhe Wang, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Data Mining
Number of pages6
ISBN (Electronic)978-1-4799-4302-9
DOIs
Publication statusPublished - 29 Jan 2015

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    Luo, C., Pang, W., Wang, Z., & Lin, C. (2015). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. In 2014 IEEE International Conference on Data Mining https://doi.org/10.1109/ICDM.2014.64