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Counterfactual Debasing for Multi-behavior Recommendations

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Database Systems for Advanced Applications (DASFAA 2024)

Abstract

Multi-behavior recommendations (MBRs) aim to enhance recommendation performance with multi-typed user-item interactions. This paper approaches MBR from a causal perspective, treating the predictions of MBR as outcomes, given various user behavioral data as treatments. However, with the incorporation of additional user behaviors, MBR becomes more vulnerable to including spurious correlations caused by unobserved confounders. Addressing such unobserved confounding effects with the current methods of frontdoor adjustment and proxy variables poses practical challenges in real-world MBRs. To solve these practical challenges, we debias the negative effects of unobserved confounders with stable counterfactual reasoning, which models the stable trend within the stratum of users and is enhanced with counterfactual examples. Specifically, we propose a counterfactual-enhanced multi-behavior recommender (C-MBR), which models user preferences from multi-behavior interactions and provides recommendations via stable counterfactual reasoning. Experiments on two real-world recommendation datasets demonstrate that our C-MBR outperforms baseline models in recommendation performance. The source code is available\(^1\)(https://github.com/s1ruihuang/c-mbr).

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Notes

  1. 1.

    https://tianchi.aliyun.com/dataset/42

  2. 2.

    https://tianchi.aliyun.com/dataset/649

References

  1. Cheng, Z., Han, S., Liu, F., Zhu, L., Gao, Z., Peng, Y.: Multi-behavior recommendation with cascading graph convolution networks. In: Proceedings of the ACM Web Conference 2023. p. 1181-1189. WWW ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3543507.3583439, https://doi.org/10.1145/3543507.3583439

  2. Jin, B., Gao, C., He, X., Jin, D., Li, Y.: Multi-behavior recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 659–668 (2020)

    Google Scholar 

  3. Li, Q., Wang, X., Wang, Z., Xu, G.: Be causal: De-biasing social network confounding in recommendation. ACM Trans. Knowl. Discov. Data 17(1) (feb 2023). https://doi.org/10.1145/3533725, https://doi.org/10.1145/3533725

  4. Li, Q., Ma, H., Zhang, R., Jin, W., Li, Z.: Dual-view co-contrastive learning for multi-behavior recommendation. Applied Intelligence pp. 1–18 (2023)

    Google Scholar 

  5. Li, Y., Sun, X., Chen, H., Zhang, S., Yang, Y., Xu, G.: Attention is not the only choice: Counterfactual reasoning for path-based explainable recommendation. arXiv preprint arXiv:2401.05744 (2024)

  6. Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, USA, 2nd edn. (2009)

    Book  MATH  Google Scholar 

  7. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. p. 452-461. AUAI Press, Arlington, Virginia, USA (2009)

    Google Scholar 

  8. Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. pp. 593–607. Springer (2018)

    Google Scholar 

  9. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. p. 1441-1450. CIKM ’19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3357384.3357895, https://doi.org/10.1145/3357384.3357895

  10. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)

    Google Scholar 

  11. Vlontzos, A., Kainz, B., Gilligan-Lee, C.M.: Estimating categorical counterfactuals via deep twin networks. Nature Machine Intelligence 5(2), 159–168 (2023)

    Article  Google Scholar 

  12. Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 165-174. SIGIR’19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3331184.3331267, https://doi.org/10.1145/3331184.3331267

  13. Wang, X., Li, Q., Yu, D., Li, Q., Xu, G.: Reinforced path reasoning for counterfactual explainable recommendation. IEEE Transactions on Knowledge and Data Engineering (2024)

    Google Scholar 

  14. Wang, Z., Shen, S., Wang, Z., Chen, B., Chen, X., Wen, J.R.: Unbiased sequential recommendation with latent confounders. In: Proceedings of the ACM Web Conference 2022. p. 2195-2204. WWW ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3485447.3512092, https://doi.org/10.1145/3485447.3512092

  15. Wang, Z., Zhang, J., Xu, H., Chen, X., Zhang, Y., Zhao, W.X., Wen, J.R.: Counterfactual data-augmented sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 347-356. SIGIR ’21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3404835.3462855, https://doi.org/10.1145/3404835.3462855

  16. Wu, Y., Xie, R., Zhu, Y., Ao, X., Chen, X., Zhang, X., Zhuang, F., Lin, L., He, Q.: Multi-view multi-behavior contrastive learning in recommendation. In: International Conference on Database Systems for Advanced Applications. pp. 166–182. Springer (2022)

    Google Scholar 

  17. Xia, L., Huang, C., Xu, Y., Dai, P., Lu, M., Bo, L.: Multi-behavior enhanced recommendation with cross-interaction collaborative relation modeling. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). pp. 1931–1936. IEEE (2021)

    Google Scholar 

  18. Xia, L., Xu, Y., Huang, C., Dai, P., Bo, L.: Graph meta network for multi-behavior recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. pp. 757–766 (2021)

    Google Scholar 

  19. Xu, G., Duong, T., Li, Q., Liu, S., Wang, X.: Causality learning: A new perspective for interpretable machine learning. IEEE Intelligent Informatics Bulletin (2020)

    Google Scholar 

  20. Xu, S., Tan, J., Heinecke, S., Li, V.J., Zhang, Y.: Deconfounded causal collaborative filtering. ACM Transactions on Recommender Systems (2021)

    Google Scholar 

  21. Xuan, H., Li, B.: Temporal-aware multi-behavior contrastive recommendation. In: International Conference on Database Systems for Advanced Applications. pp. 269–285. Springer (2023)

    Google Scholar 

  22. Xuan, H., Liu, Y., Li, B., Yin, H.: Knowledge enhancement for contrastive multi-behavior recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. pp. 195–203 (2023)

    Google Scholar 

  23. Yang, H., Chen, H., Li, L., Philip, S.Y., Xu, G.: Hyper meta-path contrastive learning for multi-behavior recommendation. In: 2021 IEEE International Conference on Data Mining (ICDM). pp. 787–796. IEEE (2021)

    Google Scholar 

  24. Yang, H., Chen, H., Zhang, S., Sun, X., Li, Q., Zhao, X., Xu, G.: Generating counterfactual hard negative samples for graph contrastive learning. In: Proceedings of the ACM Web Conference 2023. p. 621-629. WWW ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3543507.3583499, https://doi.org/10.1145/3543507.3583499

  25. Yu, D., Li, Q., Wang, X., Xu, G.: Deconfounded recommendation via causal intervention. Neurocomputing 529, 128–139 (2023)

    Article  Google Scholar 

  26. Yuan, E., Guo, W., He, Z., Guo, H., Liu, C., Tang, R.: Multi-behavior sequential transformer recommender. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 1642-1652. SIGIR ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3477495.3532023, https://doi.org/10.1145/3477495.3532023

  27. Zhang, J., Zhang, Q., Ai, Z., Li, X.: Context-based user typicality collaborative filtering recommendation. Human-Centric Intelligent Systems 1, 43–53 (2021). https://doi.org/10.2991/hcis.k.210524.001, https://doi.org/10.2991/hcis.k.210524.001

  28. Zhang, Q., Zhang, X., Liu, Y., Wang, H., Gao, M., Zhang, J., Guo, R.: Debiasing recommendation by learning identifiable latent confounders. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. p. 3353-3363. KDD ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3580305.3599296, https://doi.org/10.1145/3580305.3599296

  29. Zhu, X., Zhang, Y., Feng, F., Yang, X., Wang, D., He, X.: Mitigating hidden confounding effects for causal recommendation. arXiv preprint arXiv:2205.07499 (2022)

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grants No.62072257, the Australian Research Council Under Grants DP22010371, LE220100078, and the Hong Kong Research Grants Council under General Research Fund (Project number: 15200021).

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Correspondence to Guandong Xu or Qing Li .

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Huang, S., Li, Q., Wang, X., Yu, D., Xu, G., Li, Q. (2025). Counterfactual Debasing for Multi-behavior Recommendations. In: Onizuka, M., et al. Database Systems for Advanced Applications. DASFAA 2024. Lecture Notes in Computer Science, vol 14852. Springer, Singapore. https://doi.org/10.1007/978-981-97-5555-4_11

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  • DOI: https://doi.org/10.1007/978-981-97-5555-4_11

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