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

Cheng Luo, Wei Pang, Zhe Wang, Chenghua Lin

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

31 Citations (Scopus)
4 Downloads (Pure)

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 (ICDM 2014)
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Explore
Pages917-922
Number of pages6
ISBN (Electronic)9781479943029
ISBN (Print)9781479943012, 9781479943036
DOIs
Publication statusPublished - 24 Dec 2014
Event14th IEEE International Conference on Data Mining - Shenzhen, China
Duration: 14 Dec 201417 Dec 2014

Publication series

NameIEEE proceedings
PublisherIEEE Xplore
ISSN (Print)1550-4786

Conference

Conference14th IEEE International Conference on Data Mining
Abbreviated titleICDM 2014
CountryChina
CityShenzhen
Period14/12/1417/12/14

Fingerprint

Collaborative filtering

Cite this

Luo, C., Pang, W., Wang, Z., & Lin, C. (2014). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. In R. Kumar, H. Toivonen, J. Pei, J. Z. Huang, & X. Wu (Eds.), 2014 IEEE International Conference on Data Mining (ICDM 2014) (pp. 917-922). (IEEE proceedings). IEEE Explore. https://doi.org/10.1109/ICDM.2014.64

Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. / Luo, Cheng; Pang, Wei; Wang, Zhe; Lin, Chenghua.

2014 IEEE International Conference on Data Mining (ICDM 2014). ed. / Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu. IEEE Explore, 2014. p. 917-922 (IEEE proceedings).

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

Luo, C, Pang, W, Wang, Z & Lin, C 2014, Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. in R Kumar, H Toivonen, J Pei, JZ Huang & X Wu (eds), 2014 IEEE International Conference on Data Mining (ICDM 2014). IEEE proceedings, IEEE Explore, pp. 917-922, 14th IEEE International Conference on Data Mining, Shenzhen, China, 14/12/14. https://doi.org/10.1109/ICDM.2014.64
Luo C, Pang W, Wang Z, Lin C. Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. In Kumar R, Toivonen H, Pei J, Huang JZ, Wu X, editors, 2014 IEEE International Conference on Data Mining (ICDM 2014). IEEE Explore. 2014. p. 917-922. (IEEE proceedings). https://doi.org/10.1109/ICDM.2014.64
Luo, Cheng ; Pang, Wei ; Wang, Zhe ; Lin, Chenghua. / Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. 2014 IEEE International Conference on Data Mining (ICDM 2014). editor / Ravi Kumar ; Hannu Toivonen ; Jian Pei ; Joshua Zhexue Huang ; Xindong Wu. IEEE Explore, 2014. pp. 917-922 (IEEE proceedings).
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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.",
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