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Personalized recommendation on multi-layer context graph
conference contribution
posted on 2013-11-18, 00:00 authored by W Yao, J He, Guangyan HuangGuangyan Huang, J Cao, Y ZhangRecommender systems have been successfully dealing with the problem of information overload. A considerable amount of research has been conducted on recommender systems, but most existing approaches only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a Multi-Layer Context Graph (MLCG) model which incorporates a variety of contextual information into a recommendation process and models the interactions between users and items for better recommendation. Moreover, we provide a new ranking algorithm based on Personalized PageRank for recommendation in MLCG, which captures users' preferences and current situations. The experiments on two real-world datasets demonstrate the effectiveness of our approach. © 2013 Springer-Verlag.
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Volume
8180 LNCSIssue
PART 1Pagination
135 - 148Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642412295Publication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2013, SpringerTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
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