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

Chen Luo; Wei Pang; Zhe Wang; Chenghua Lin

doi:10.1109/ICDM.2014.64

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 proceeding › Conference contribution

110 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 language	English
Title of host publication	2014 IEEE International Conference on Data Mining
Number of pages	6
ISBN (Electronic)	978-1-4799-4302-9
DOIs	https://doi.org/10.1109/ICDM.2014.64
Publication status	Published - 29 Jan 2015

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@inproceedings{1584168966d9487aa674670b694f359b,

title = "Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations",

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.",

author = "Chen Luo and Wei Pang and Zhe Wang and Chenghua Lin",

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.",

year = "2015",

month = jan,

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doi = "10.1109/ICDM.2014.64",

language = "English",

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T2 - Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations

AU - Luo, Chen

AU - Pang, Wei

AU - Wang, Zhe

AU - Lin, Chenghua

N1 - 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.

PY - 2015/1/29

Y1 - 2015/1/29

N2 - 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.

AB - 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.

U2 - 10.1109/ICDM.2014.64

DO - 10.1109/ICDM.2014.64

M3 - Conference contribution

SN - 9781479943036

BT - 2014 IEEE International Conference on Data Mining

ER -

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

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