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.Preprin

Lin, Chenghua

Luo, Cheng

Pang, Wei

Wang, Zhe

English

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, Chen

Heriot Watt Pure

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

Chen Luo

Wei Pang

Zhe Wang

Chenghua Lin

Crossref

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

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

Aberdeen University Research

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

https://aura.abdn.ac.uk/bitstream/2164/4281/1/ICDM2014SelfArchive.pdf

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

Abstract

Similar works

Full text

Available Versions

Heriot Watt Pure

Crossref

Heriot Watt Pure

Aberdeen University Research