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Preference relation-based markov random fields

Liu, Shaowu, Li, Gang, Tran, Truyen and Yuan, Jiang 2016, Preference relation-based markov random fields, in ACML 2015 : Proceedings of 7th Asian Conference on Machine Learning, JMLR: Workshop and Conference Proceedings series, [Hong Kong], pp. 1-16.

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Title Preference relation-based markov random fields
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
Yuan, Jiang
Conference name Asian Conference on Machine Learning (7th : 2015 : Hong Kong)
Conference location Hong Kong
Conference dates 20-22 Nov. 2015
Title of proceedings ACML 2015 : Proceedings of 7th Asian Conference on Machine Learning
Editor(s) Holmes, G.
Liu, T.Y.
Publication date 2016
Start page 1
End page 16
Total pages 16
Publisher JMLR: Workshop and Conference Proceedings series
Place of publication [Hong Kong]
Keyword(s) preference relation
pairwise preference
Markov random fields
collaborative filtering
recommender systems
Summary 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 fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs 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 signifi cantly improved Top-N recommendation performance has been achieved.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081482

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Created: Mon, 07 Mar 2016, 11:28:25 EST

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