Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

• URL: http://arxiv.org/abs/2003.01892v1
• Date: Wed, 4 Mar 2020 04:50:44 GMT
• Title: Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
• Authors: Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
• Abstract summary: How to assign confidence weights and how to handle the large number of unobserved data are two key problems for implicit recommendation models. We propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. Experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.
• Score: 28.30678887024847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.

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