Abstract
Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists or book lists. Several graph-based models have achieved state-of-the-art performance on BR task. But their performance is still sub-optimal, since the data sparsity problem tends to be more severe in real BR scenarios, which limits these models from more sufficient learning. In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve BR by introducing counterfactual thinking. Our paradigm consists of two main parts: counterfactual data augmentation and counterfactual constraint. In counterfactual data augmentation, we design a heuristic sampler to generate counterfactual graph views for graph-based models to alleviate the data sparsity. We further propose counterfactual loss to constrain model learning for mitigating the effects of noise in augmented data and achieving more sufficient model optimization. Further theoretical analysis demonstrates the rationality of our design. Extensive experiments of BR models applied with our paradigm on two real-world datasets are conducted to verify the effectiveness of the paradigm.
S. Zhu and Q. Shen—Both authors contributed equally to this research.
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Abbasnejad, E., Teney, D., Parvaneh, A., Shi, J., Hengel, A.V.D.: Counterfactual vision and language learning. In: CVPR, pp. 10044–10054 (2020)
Berg, R.V.D., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)
Cao, D., Nie, L., He, X., Wei, X., Zhu, S., Chua, T.S.: Embedding factorization models for jointly recommending items and user generated lists. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 585–594 (2017)
Cao, D., Nie, L., He, X., Wei, X., Zhu, S., Chua, T.S.: Embedding factorization models for jointly recommending items and user generated lists. In: SIGIR (2017)
Chang, J., Gao, C., He, X., Jin, D., Li, Y.: Bundle recommendation with graph convolutional networks. In: SIGIR, pp. 1673–1676 (2020)
Chen, L., Liu, Y., He, X., Gao, L., Zheng, Z.: Matching user with item set: Collaborative bundle recommendation with deep attention network. In: IJCAI (2019)
Deng, Q., et al.: Build your own bundle - a neural combinatorial optimization method (2021)
Deng, Q., et al.: Personalized bundle recommendation in online games. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: ICML, vol. 97, pp. 2376–2384 (2019)
Li, X., Wang, X., He, X., Chen, L., Xiao, J., Chua, T.S.: Hierarchical fashion graph network for personalized outfit recommendation. In: SIGIR, pp. 159–168 (2020)
Liu, G., Fu, Y., Chen, G., Xiong, H., Chen, C.: Modeling buying motives for personalized product bundle recommendation. TKDD 11(3), 1–26 (2017)
Liu, Y., Xie, M., Lakshmanan, L.V.: Recommending user generated item lists. In: RecSys, pp. 185–192 (2014)
Ma, Y., He, Y., Zhang, A., Wang, X., Chua, T.S.: Crosscbr: cross-view contrastive learning for bundle recommendation. arXiv preprint arXiv:2206.00242 (2022)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)
Sar Shalom, O., Koenigstein, N., Paquet, U., Vanchinathan, H.P.: Beyond collaborative filtering: the list recommendation problem. In: WWW, pp. 63–72 (2016)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)
Sui, Y., Wang, X., Wu, J., He, X., Chua, T.S.: Deconfounded training for graph neural networks. arXiv preprint arXiv:2112.15089 (2021)
Wang, W., Feng, F., He, X., Zhang, H., Chua, T.S.: Clicks can be cheating: counterfactual recommendation for mitigating clickbait issue. In: SIGIR (2021)
Wang, Z., Zhang, J., Xu, H., Chen, X., Zhang, Y., et al.: Counterfactual data-augmented sequential recommendation. In: SIGIR, pp. 347–356 (2021)
Xiong, K., et al.: Counterfactual review-based recommendation. In: CIKM, pp. 2231–2240 (2021)
Xu, S., Ge, Y., Li, Y., Fu, Z., Chen, X., Zhang, Y.: Causal collaborative filtering. arXiv preprint arXiv:2102.01868 (2021)
Yuan, X., Chen, H., Song, Y., Zhao, X., Ding, Z., et al.: Improving sequential recommendation consistency with self-supervised imitation. In: IJCAI (2021)
Zhang, J., Chen, X., Zhao, W.X.: Causally attentive collaborative filtering. In: CIKM, pp. 3622–3626 (2021)
Zhang, S., Yao, D., Zhao, Z., Chua, T.S., Wu, F.: Causerec: counterfactual user sequence synthesis for sequential recommendation. In: SIGIR, pp. 367–377 (2021)
Zhang, Y., et al.: Causal intervention for leveraging popularity bias in recommendation. In: SIGIR (2021)
Zhu, T., Harrington, P., Li, J., Tang, L.: Bundle recommendation in ecommerce. In: SIGIR, pp. 657–666 (2014)
Zmigrod, R., Mielke, S.J., Wallach, H., Cotterell, R.: Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. arXiv preprint arXiv:1906.04571 (2019)
Acknowledgments
The work is partially supported by the National Nature Science Foundation of China (No. 61976160, 61906137, 61976158, 62076184, 62076182), the Natural Science Foundation of Shanghai (Grant No. 22ZR1466700), Shanghai Science and Technology Plan Project (No. 21DZ1204800) and Technology research plan project of Ministry of Public and Security (No. 2020JSYJD01).
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Zhu, S. et al. (2023). Data-Augmented Counterfactual Learning for Bundle Recommendation. In: El Abbadi, A., et al. Database Systems for Advanced Applications. DASFAA 2023 International Workshops. DASFAA 2023. Lecture Notes in Computer Science, vol 13922. Springer, Cham. https://doi.org/10.1007/978-3-031-35415-1_22
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