Causal Intervention for Leveraging Popularity Bias in Recommendation

By Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

535 citations recorded by the source corpus.

Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods are prone to amplify the bias by over-recommending popular items. It is undoubtedly critical to consider popularity bias in recommender systems, and existing work mainly eliminates the bias effect with propensity-based unbiased learning or causal embeddings. However, we argue that not all biases in the data are bad, \ie some items demonstrate higher popularity because of their better intrinsic quality. Blindly pursuing unbiased learning may remove the beneficial patterns in the data, degrading the recommendation accuracy and user satisfaction. This work studies an unexplored problem in recommendation --- how to leverage popularity bias to improve the recommendation accuracy. The key lies in two aspects: how to remove the bad impact of popularity bias during training, and how to inject the desired popularity bias in the inference stage that generates top-K recommendations. This questions the causal mechanism of the recommendation generation process. Along this line, we find that item popularity plays the role ofconfounder between the exposed items and the observed interactions, causing the bad effect of bias amplification. To achieve our goal, we propose a new training and inference paradigm for recommendation named Popularity-bias Deconfounding and Adjusting (PDA). It removes the confounding popularity bias in model training and adjusts the recommendation score with desired popularity bias via causal intervention. We demonstrate the new paradigm on the latent factor model and perform extensive experiments on three real-world datasets from Kwai, Douban, and Tencent. Empirical studies validate that the deconfounded training is helpful to discover user real interests and the inference adjustment with popularity bias could further improve the recommendation accuracy. We release our code at https://github.com/zyang1580/PDA.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/afea12302cab67757e61dfb13aec6d864ccbfc4b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Xiangnan He
  2. Xiang Wang
  3. Fuli Feng
  4. Meng Wang
  5. Tat-seng Chua
  6. Jiawei Chen
  7. Hande Dong
  8. Liqiang Nie

In Information Systems

  1. Xiangnan He
  2. Xiang Wang
  3. Fuli Feng
  4. Meng Wang
  5. Tat-seng Chua
  6. Jiawei Chen
  7. Hande Dong
  8. Liqiang Nie

In Management Science and Operations Research

  1. Xiangnan He
  2. Xiang Wang
  3. Meng Wang
  4. Fuli Feng
  5. Jiawei Chen
  6. Hande Dong
  7. Tat-seng Chua
  8. Weiwei Jiang

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