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Improved time-aware debiasing models for recommender systems
- 1 Cranbrook Schools
- 2 Dalian University of Technology
- 3 The Affiliated High School of Peking University
- 4 The University of Sydney
- 5 University of California
- 6 University College Dublin
* Author to whom correspondence should be addressed.
Abstract
Recommender systems are affected by user selection bias during user interactions. Previously, Huang, Jin et al. proposed the TMF-DANCER model to deal with the fact that selection bias is dynamic, and the popularity of an item and user preferences may change drastically over time. However, the previous time-aware methods did not consider the continuity of time, and performing collaborative filtering with matrix factorization may not be able to capture the complex structure of user interaction data. Our methods enhance the effectiveness of time-aware debiasing by utilizing the time-varying sequential method to alleviate the discontinuity of time-based on the frequency-based method, Replacing the matrix factorization with neural collaborative filtering (NCF). We used movie lens 100k as the data for training. The experimental results indicate that methods incorporating the time-varying sequential method and NCF have better performance in rating prediction than debiasing methods without.
Keywords
Selection bias, Dynamic user preferences, Time-varying sequential method, Time-aware Neural Collaborative Filtering
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Cite this article
Wang,S.;Wu,X.;Zhao,L.;Xue,Y.;Xia,R.;Sun,Z. (2023). Improved time-aware debiasing models for recommender systems. Applied and Computational Engineering,6,1560-1576.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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