Abstract
How can we expose diverse items across all users while satisfying their needs in bundle recommendations? Diversified bundle recommendation is a crucial task since it leads to great benefits for both sellers and users. However, there have been no studies on aggregate diversity in bundle recommendation, while they have been intensively studied in item recommendation. Moreover, existing methods of aggregately diversified item recommendation are not fully suitable for bundle recommendation. In this paper, we propose PopCon (Popularity Debiasing and Configuration-aware Reranking), an accurate method for aggregately diversified bundle recommendation. PopCon mitigates the popularity bias of a recommendation model by a popularity-based negative sampling in training process, and maximizes accuracy and aggregate diversity by a configuration-aware reranking algorithm. We show that PopCon provides state-of-the-art performance on real-world datasets, achieving up to \(60.5\%\) higher Entropy@5 and \(3.92\times \) higher Coverage@5 with comparable accuracies compared to the best competitor.
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References
Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: RecSys (2017)
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. (2012)
Cao, D., Nie, L., He, X., Wei, X., Zhu, S., Chua, T.: 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 (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.: Personalized bundle recommendation in online games. In: CIKM (2020)
Dong, Q., Xie, S., Li, W.: User-item matching for recommendation fairness. IEEE Access (2021)
Jeon, H., Jang, J.G., Kim, T., Kang, U.: Accurate bundle matching and generation via multitask learning with partially shared parameters. Plos one (2023)
Jeon, H., Kim, J., Yoon, H., Lee, J., Kang, U.: Accurate action recommendation for smart home via two-level encoders and commonsense knowledge. In: CIKM. ACM (2022)
Jeon, H., Koo, B., Kang, U.: Data context adaptation for accurate recommendation with additional information. In: BigData (2019)
Karakaya, M.Ö., Aytekin, T.: Effective methods for increasing aggregate diversity in recommender systems. Knowl. Inf. Syst. (2018)
Kim, J., Jeon, H., Lee, J., Kang, U.: Diversely regularized matrix factorization for accurate and aggregately diversified recommendation. In: PAKDD (2023)
Koo, B., Jeon, H., Kang, U.: Accurate news recommendation coalescing personal and global temporal preferences. In: PAKDD (2020)
Ma, Y., He, Y., Zhang, A., Wang, X., Chua, T.: Crosscbr: Cross-view contrastive learning for bundle recommendation. In: KDD (2022)
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., Burke, R.: Fairmatch: A graph-based approach for improving aggregate diversity in recommender systems. In: UMAP (2020)
Park, H., Jung, J., Kang, U.: A comparative study of matrix factorization and random walk with restart in recommender systems. In: BigData (2017)
Park, Y., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: RecSys (2008)
Pathak, A., Gupta, K., McAuley, J.J.: Generating and personalizing bundle recommendations on Steam. In: SIGIR (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI (2009)
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. (2019)
Acknowledgments
This work was supported by Jung-Hun Foundation. The Institute of Engineering Research and ICT at Seoul National University provided research facilities for this work. U Kang is the corresponding author.
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Jeon, H., Kim, J., Lee, J., Lee, Je., Kang, U. (2023). Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-Aware Reranking. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13937. Springer, Cham. https://doi.org/10.1007/978-3-031-33380-4_27
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DOI: https://doi.org/10.1007/978-3-031-33380-4_27
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