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Poster
Curriculum Disentangled Recommendation with Noisy Multi-feedback
Hong Chen · Yudong Chen · Xin Wang · Ruobing Xie · Rui Wang · Feng Xia · Wenwu Zhu

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

Learning disentangled representations for user intentions from multi-feedback (i.e., positive and negative feedback) can enhance the accuracy and explainability of recommendation algorithms. However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e.g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance. Existing works on disentangled representation learning only focus on positive feedback, failing to handle the complex relations and noise hidden in multi-feedback data. To solve this problem, in this work we propose a Curriculum Disentangled Recommendation (CDR) model that is capable of efficiently learning disentangled representations from complex and noisy multi-feedback for better recommendation. Concretely, we design a co-filtering dynamic routing mechanism that simultaneously captures the complex relations among different behavioral feedback and user intentions as well as denoise the representations in the feature level. We then present an adjustable self-evaluating curriculum that is able to evaluate sample difficulties for better model training and conduct denoising in the label level via disregarding useless information. Our extensive experiments on several real-world datasets demonstrate that the proposed CDR model can significantly outperform several state-of-the-art methods in terms of recommendation accuracy.

Author Information

Hong Chen (Tsinghua University)
Yudong Chen (Tsinghua University, Tsinghua University)
Xin Wang (Tsinghua University)
Ruobing Xie (WeChat, Tencent)
Rui Wang (Tencent Wechat Group)
Feng Xia (Institute of Computing Technology, CAS)
Wenwu Zhu (Tsinghua University)

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