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Preference relation-based Markov Random Fields for recommender systems

Liu, Shaowu, Li, Gang, Tran, Truyen and Jiang, Yuan 2017, Preference relation-based Markov Random Fields for recommender systems, Machine learning, vol. 106, no. 4, pp. 523-546, doi: 10.1007/s10994-016-5603-7.

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Title Preference relation-based Markov Random Fields for recommender systems
Author(s) Liu, Shaowu
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Jiang, Yuan
Journal name Machine learning
Volume number 106
Issue number 4
Start page 523
End page 546
Total pages 24
Publisher Springer
Place of publication New York, N.Y.
Publication date 2017-04
ISSN 0885-6125
1573-0565
Keyword(s) recommender systems
collaborative filtering
preference relation
pairwise preference
Markov Random Fields
Summary A preference relation-based Top-N recommendation approach is proposed to capture both second-order and 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 feedback 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 approach drops these assumptions by exploiting preference relations, a more practical user feedback. Furthermore, the proposed approach enjoys the representational power of Markov Random Fields thus side information such as item and user attributes can be easily incorporated. Comparing to related work, the proposed approach has the unique property of modeling both second-order and 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 methods. Experimental results on public datasets demonstrate that both types of interactions have been properly captured, and significantly improved Top-N recommendation performance has been achieved.
Language eng
DOI 10.1007/s10994-016-5603-7
Field of Research 080399 Computer Software not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090170

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Created: Fri, 09 Dec 2016, 11:49:27 EST

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