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

Cheng Luo; Wei Pang; Zhe Wang; Chenghua Lin

doi:10.1109/ICDM.2014.64

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

Cheng Luo
, Wei Pang
, Zhe Wang
, Chenghua Lin

Computing Science

Jilin University

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

151 Citations (Scopus)

23 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 language	English
Title of host publication	2014 IEEE International Conference on Data Mining
Editors	Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Publisher	IEEE Explore
Pages	917-922
Number of pages	6
ISBN (Electronic)	9781479943029
ISBN (Print)	9781479943012, 9781479943036
DOIs	https://doi.org/10.1109/ICDM.2014.64
Publication status	Published - 24 Dec 2014
Event	14th IEEE International Conference on Data Mining - Shenzhen, China Duration: 14 Dec 2014 → 17 Dec 2014

Publication series

Name	IEEE proceedings
Publisher	IEEE Xplore
ISSN (Print)	1550-4786
ISSN (Electronic)	2374-8486

Conference

Conference	14th IEEE International Conference on Data Mining
Abbreviated title	ICDM 2014
Country/Territory	China
City	Shenzhen
Period	14/12/14 → 17/12/14

Bibliographical note

The work described here was funded by the National Natural Science
Foundation of China (NSFC) under Grant No. 61373051; the National Science
and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory
for Symbolic Computation and Knowledge Engineering, Ministry of
Education, China, and the UK Economic & Social Research Council (ESRC);
award reference: ES/M001628/1.

Access to Document

10.1109/ICDM.2014.64Licence: Unspecified

ICDM2014SelfArchive
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Submitted manuscript, 184 KB

http://arxiv.org/abs/1412.7610Licence: CC BY

Cite this

Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. / Luo, Cheng; Pang, Wei; Wang, Zhe et al.
2014 IEEE International Conference on Data Mining. 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 proceeding › Published conference 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. 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

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

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Bibliographical note

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