Mixture-preference bayesian matrix factorization for implicit feedback datasets
Pages 1427 - 1434
Abstract
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.
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Index Terms
- Mixture-preference bayesian matrix factorization for implicit feedback datasets
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Published In
March 2020
2348 pages
ISBN:9781450368667
DOI:10.1145/3341105
- Conference Chairs:
- Chih-Cheng Hung,
- Tomas Cerny,
- Program Chairs:
- Dongwan Shin,
- Alessio Bechini
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Published: 30 March 2020
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SAC '20
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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing
March 30 - April 3, 2020
Brno, Czech Republic
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