Mixture-preference bayesian matrix factorization for implicit feedback datasets

Shaowen Peng, Tsunenori Mine

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recommendation with implicit feedback has been extensively studied in recent years. It is more difficult to provide users with stable and accurate recommendation compared to recommendation with explicit feedback, due to the reason that interactions from implicit feedback datasets do not clearly indicate the level of user preference. Most existing methods dealing with implicit feedback have achieved excellent performance by focusing on other aspects rather than directly inferring user preference. In this paper, we offer accurate recommendation to users by addressing the problem of directly inferring user preference from implicit feedback with such less information and huge uncertainty. We propose a novel mixture-preference model (MPBMF), which introduces a set of pseudo-preference values to surmise the true user preference. More specifically our proposed model can be described as a Gaussian mixture model in which each single model is trained with pseudo-preferences which show the user's different views for items. Then the predicted user preference is estimated by the models under different pseudo-preferences with different contributions. We conduct extensive experiments on three real-world datasets, and the superior performance demonstrates the effectiveness of our model.

Original languageEnglish
Title of host publication35th Annual ACM Symposium on Applied Computing, SAC 2020
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450368667
Publication statusPublished - Mar 30 2020
Event35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
Duration: Mar 30 2020Apr 3 2020

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference35th Annual ACM Symposium on Applied Computing, SAC 2020
Country/TerritoryCzech Republic

All Science Journal Classification (ASJC) codes

  • Software


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