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Invariant representation learning to popularity distribution shift for recommendation

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Abstract

Recommender systems often suffer from severe performance drops due to popularity distribution shift (PDS), which arises from inconsistencies in item popularity between training and test data. Most existing methods aimed at mitigating PDS focus on reducing popularity bias, but they usually require inaccessible information or rely on implausible assumptions. To solve the above problem, in this work, we propose a novel framework called Invariant Representation Learning (IRL) to PDS. Specifically, for simulating diverse popularity environments where popular items and active users become even more popular and active, or conversely, we apply perturbations to the user-item interaction matrix by adjusting the weights of popular items and active users in the matrix, without any prior assumptions or specialized information. In different simulated popularity environments, dissimilarities in the distribution of representations for items and users occur. We further utilize contrastive learning to minimize the dissimilarities among the representations of users and items under different simulated popularity environments, resulting in invariant representations that remain consistent across varying popularity distributions. Extensive experiments on three real-world datasets demonstrate that IRL outperforms state-of-the-art baselines in effectively alleviating PDS for recommendation.

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All datasets used in this study are publicly available.

References

  1. He, Y., Wang, Z., Cui, P., Zou, H., Zhang, Y., Cui, Q., Jiang, Y.: Causpref: causal preference learning for out-of-distribution recommendation. In: Proceedings of the ACM Web Conference 2022, pp. 410–421 (2022)

  2. Gao, C., Li, S., Zhang, Y., Chen, J., Li, B., Lei, W., Jiang, P., He, X.: Kuairand: an unbiased sequential recommendation dataset with randomly exposed videos. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 3953–3957 (2022)

  3. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618 (2012)

  4. Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X.: Bias and debias in recommender system: a survey and future directions. ACM Trans. Inf. Syst. 41(3), 1–39 (2023)

    Google Scholar 

  5. Chen, J., Wu, J., Chen, J., Xin, X., Li, Y., He, X.: How graph convolutions amplify popularity bias for recommendation? arXiv:2305.14886 (2023)

  6. Hong, Y., Yuan, X., Li, X.: Dcl4rec: an effective debiased contrastive learning framework for long-tail sequential recommendation. Available at SSRN 4558746

  7. Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 42–46 (2017)

  8. Boratto, L., Fenu, G., Marras, M.: Connecting user and item perspectives in popularity debiasing for collaborative recommendation. Inf. Process. Manag. 58(1), 102387 (2021)

    Article  Google Scholar 

  9. Chen, Z., Xiao, R., Li, C., Ye, G., Sun, H., Deng, H.: Esam: discriminative domain adaptation with non-displayed items to improve long-tail performance. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 579–588 (2020)

  10. Zhu, Z., He, Y., Zhao, X., Zhang, Y., Wang, J., Caverlee, J.: Popularity-opportunity bias in collaborative filtering. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 85–93 (2021)

  11. Chen, J., Dong, H., Qiu, Y., He, X., Xin, X., Chen, L., Lin, G., Yang, K.: Autodebias: learning to debias for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 21–30 (2021)

  12. Ding, S., Wu, P., Feng, F., Wang, Y., He, X., Liao, Y., Zhang, Y.: Addressing unmeasured confounder for recommendation with sensitivity analysis. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 305–315 (2022)

  13. Gruson, A., Chandar, P., Charbuillet, C., McInerney, J., Hansen, S., Tardieu, D., Carterette, B.: Offline evaluation to make decisions about playlistrecommendation algorithms. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 420–428 (2019)

  14. Wang, W., Feng, F., He, X., Wang, X., Chua, T.-S.: Deconfounded recommendation for alleviating bias amplification. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1717–1725 (2021)

  15. Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., Zhang, Y.: Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11–20 (2021)

  16. Yu, J., Xia, X., Chen, T., Cui, L., Hung, N.Q.V., Yin, H.: Xsimgcl: towards extremely simple graph contrastive learning for recommendation. IEEE Trans. Knowl, Data Eng (2023)

    Google Scholar 

  17. Wang, Z., He, Y., Liu, J., Zou, W., Yu, P.S., Cui, P.: Invariant preference learning for general debiasing in recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1969–1978 (2022)

  18. Zhang, A., Zheng, J., Wang, X., Yuan, Y., Chua, T.-S.: Invariant collaborative filtering to popularity distribution shift. In: Proceedings of the ACM Web Conference 2023, pp. 1240–1251 (2023)

  19. Wang, W., Lin, X., Wang, L., Feng, F., Ma, Y., Chua, T.-S.: Causal disentangled recommendation against user preference shifts. ACM Trans. Inf, Syst (2023)

    Google Scholar 

  20. Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., Courville, A.: Out-of-distribution generalization via risk extrapolation (rex). In: International Conference on Machine Learning, pp. 5815–5826, PMLR (2021)

  21. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639–648 (2020)

  22. Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv:1907.02893 (2019)

  23. Bühlmann, P.: Invariance, causality and robustness (2020)

  24. Liu, J., Hu, Z., Cui, P., Li, B., Shen, Z.: Heterogeneous risk minimization. In: International Conference on Machine Learning, pp. 6804–6814, PMLR (2021)

  25. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189, PMLR (2015)

  26. Ahuja, K., Caballero, E., Zhang, D., Gagnon-Audet, J.-C., Bengio, Y., Mitliagkas, I., Rish, I.: Invariance principle meets information bottleneck for out-of-distribution generalization. Adv. Neural Inf. Process. Syst. 34, 3438–3450 (2021)

    Google Scholar 

  27. Liu, E.Z., Haghgoo, B., Chen, A.S., Raghunathan, A., Koh, P.W., Sagawa, S., Liang, P., Finn, C.: Just train twice: improving group robustness without training group information. In: International Conference on Machine Learning, pp. 6781–6792, PMLR (2021)

  28. Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726–735 (2021)

  29. Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.V.H.: Are graph augmentations necessary? simple graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1294–1303 (2022)

  30. Xia, L., Huang, C., Xu, Y., Zhao, J., Yin, D., Huang, J.: Hypergraph contrastive collaborative filtering. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 70–79 (2022)

  31. Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: Proceedings of the ACM Web Conference 2022, pp. 2320–2329 (2022)

  32. Cai, X., Huang, C., Xia, L., Ren, X.: Lightgcl: simple yet effective graph contrastive learning for recommendation. arXiv:2302.08191 (2023)

  33. Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv:1807.03748 (2018)

  34. Marlin, B.M., Zemel, R.S.: Collaborative prediction and ranking with non-random missing data. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 5–12 (2009)

  35. Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., Joachims, T.: Recommendations as treatments: Debiasing learning and evaluation. In: International Conference on Machine Learning, pp. 1670–1679, PMLR (2016)

  36. Bonner, S., Vasile, F.: Causal embeddings for recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104–112 (2018)

  37. 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)

  38. Chen, Z., Wu, J., Li, C., Chen, J., Xiao, R., Zhao, B.: Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 60–69 (2022)

  39. Wen, H., Yi, X., Yao, T., Tang, J., Hong, L., Chi, E.H.: Distributionally-robust recommendations for improving worst-case user experience. In: Proceedings of the ACM Web Conference 2022, pp. 3606–3610 (2022)

  40. Zhou, X., Zhou, H., Liu, Y., Zeng, Z., Miao, C., Wang, P., You, Y., Jiang, F.: Bootstrap latent representations for multi-modal recommendation. In: Proceedings of the ACM Web Conference 2023, pp. 845–854 (2023)

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, Pingleyuan, 100124, China

    Ming He, Han Zhang, Zihao Zhang & Chang Liu

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  1. Ming He
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  2. Han Zhang
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  3. Zihao Zhang
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  4. Chang Liu
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Contributions

Supervision, M.H.; Writing-original draft, H.Z; Writing-review & editing, Z.Z. and C.L.

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Correspondence to Ming He.

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This article belongs to the Topical Collection: Special Issue on Advancing recommendation systems with foundation models

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He, M., Zhang, H., Zhang, Z. et al. Invariant representation learning to popularity distribution shift for recommendation. World Wide Web 27, 10 (2024). https://doi.org/10.1007/s11280-024-01242-x

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