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Preference relation-based markov random fields
conference contribution
posted on 2016-01-01, 00:00 authored by Shaowu Liu, Gang LiGang Li, Truyen TranTruyen Tran, J YuanA 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.
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.
History
Event
Asian Conference on Machine Learning (7th : 2015 : Hong Kong)Pagination
1 - 16Publisher
JMLR: Workshop and Conference Proceedings seriesLocation
Hong KongPlace of publication
[Hong Kong]Start date
2015-11-20End date
2015-11-22Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, The AuthorsEditor/Contributor(s)
G Holmes, T LiuTitle of proceedings
ACML 2015: Proceedings of the 7th Asian Conference on Machine LearningUsage metrics
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