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Ordinal random fields for recommender systems
conference contribution
posted on 2014-11-28, 00:00 authored by Shaowu Liu, Truyen TranTruyen Tran, Gang LiGang Li, Y JiangRecommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.
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Event
Asian Conference on Machine Learning (6th : 2014 : Nha Trang, Vietnam)Volume
39Series
JMLR Workshop and Conference ProceedingsPagination
283 - 298Publisher
JMLR Workshop and Conference ProceedingsLocation
Nha Trang, VietnamPlace of publication
[Nha Trang, Vietnam]Start date
2014-11-26End date
2014-11-28eISSN
1533-7928Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, The AuthorEditor/Contributor(s)
D Phung, H LiTitle of proceedings
ACML 2014: Proceedings of the Sixth Asian Conference on Machine LearningUsage metrics
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