Abstract
Popularity bias is a common problem in recommender systems. Existing research mainly tracks this problem by re-weighting training samples or leveraging a small fraction of unbiased data. However, the effect of popularity bias in user behavior data may lead to sacrifices in recommendation. In this paper, we exploit data bias from click behavior to derive popularity bias representation, and investigate how to mitigate its negative impact from a causal perspective. Motivated by causal effects, we propose a novel counterfactual inference framework named Mitigating Popularity Bias in Recommendation via Counterfactual Inference (MPCI), which enables us to capture the popularity bias as the direct causal effect of the prediction score, and we eliminate popularity bias by subtracting the direct popularity bias effect from the total causal effect. In this way, MPCI reduces popularity bias by decreasing the influence of popular items on model training. Extensive experiments on two real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiveness of mitigating popularity bias in recommender systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bonner, S., Vasile, F.: Causal embeddings for recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104–112 (2018)
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X.: Bias and debias in recommender system: a survey and future directions. arXiv preprint arXiv:2010.03240 (2020)
Gulla, J.A., Zhang, L., Liu, P., Özgöbek, Ö., Su, X.: The adressa dataset for news recommendation. In: Proceedings of the International Conference on Web Intelligence, pp. 1042–1048 (2017)
Liu, Y., Ge, K., Zhang, X., Lin, L.: Real-time attention based look-alike model for recommender system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2765–2773 (2019)
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.S., Wen, J.R.: Counterfactual VQA: a cause-effect look at language bias. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12700–12710 (2021)
Pearl, J.: Direct and indirect effects. arXiv preprint arXiv:1301.2300 (2013)
Qian, C., Feng, F., Wen, L., Ma, C., Xie, P.: Counterfactual inference for text classification debiasing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5434–5445 (2021)
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)
Wang, W., Feng, F., He, X., Wang, X., Chua, T.S.: Deconfounded recommendation for alleviating bias amplification. arXiv preprint arXiv:2105.10648 (2021)
Wang, W., Feng, F., He, X., Zhang, H., Chua, T.S.: Clicks can be cheating: counterfactual recommendation for mitigating clickbait issue. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1288–1297 (2021)
Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., He, X.: Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1791–1800 (2021)
Wei, Y., Wang, X., Nie, L., He, X., Hong, R., Chua, T.S.: MMGCN: multi-modal graph convolution network for personalized recommendation of micro-video. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1437–1445 (2019)
Wen, H., Yang, L., Estrin, D.: Leveraging post-click feedback for content recommendations. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 278–286 (2019)
Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., Zhang, Y.: Causal intervention for leveraging popularity bias in recommendation. arXiv preprint arXiv:2105.06067 (2021)
Zheng, Y., Gao, C., Li, X., He, X., Li, Y., Jin, D.: Disentangling user interest and conformity for recommendation with causal embedding. In: Proceedings of the Web Conference 2021, pp. 2980–2991 (2021)
Acknowledgement
This work is supported by the Beijing Natural Science Foundation under grants 4192008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
He, M., Li, C., Hu, X., Chen, X., Wang, J. (2022). Mitigating Popularity Bias in Recommendation via Counterfactual Inference. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-00129-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00128-4
Online ISBN: 978-3-031-00129-1
eBook Packages: Computer ScienceComputer Science (R0)