A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings first, and then inferring the item rankings, based on the assumption of availability of explicit feedbacks such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.
History
Volume
45
Pagination
157-172
Location
Hong Kong
Start date
2015-11-20
End date
2015-11-22
ISSN
1532-4435
eISSN
1533-7928
Language
eng
Publication classification
E Conference publication, E1.1 Full written paper - refereed
Copyright notice
2015, S. Liu, G. Li, T. Tran & Y. Jiang.
Editor/Contributor(s)
Holmes G, Liu TY
Title of proceedings
ACML 2015 - 7th Asian Conference on Machine Learning
Event
Asian Conference on Machine Learning (7th : 2015 : Hong Kong)