A graph-based model for context-aware recommendation using implicit feedback data

Yao, Weilong, He, Jing, Huang, Guangyan, Cao, Jie and Zhang, Yanchun 2015, A graph-based model for context-aware recommendation using implicit feedback data, World wide web, vol. 18, no. 5, pp. 1351-1371, doi: 10.1007/s11280-014-0307-z.

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Title A graph-based model for context-aware recommendation using implicit feedback data
Author(s) Yao, Weilong
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Cao, Jie
Zhang, Yanchun
Journal name World wide web
Volume number 18
Issue number 5
Start page 1351
End page 1371
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-09
ISSN 1386-145X
Summary Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.
Language eng
DOI 10.1007/s11280-014-0307-z
Field of Research 080109 Pattern Recognition and Data Mining
0805 Distributed Computing
0806 Information Systems
Socio Economic Objective 810105 Intelligence
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30078770

Document type: Journal Article
Collection: School of Information Technology
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