Abstract
Data sparsity is a well-known challenge in recommender systems. One way to alleviate this problem is to leverage knowledge from relevant domains. In this paper, we focus on an important real-world scenario in which some users overlap two different domains but items of the two domains are distinct. Although several studies leverage side information (e.g., user reviews) for cross-domain recommendation, side information is not always available or easy to obtain in practice. To this end, we propose cross-domain preference ranking (CPR) with a simple yet effective user transformation that leverages only user interactions with items in the source and target domains to transform the user representation. Given the proposed user transformation, CPR not only successfully enhances recommendation performance for users having interactions with target-domain items but also yields superior performance for cold-start users in comparison with state-of-the-art cross-domain recommendation approaches. Extensive experiments conducted on three pairs of cross-domain recommendation datasets demonstrate the effectiveness of the proposed method in comparison with existing cross-domain recommendation approaches. Our codes are available at https://github.com/cnclabs/codes.crossdomain.rec.
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For each user, we reserved the latest interaction as the test item and randomly sampled 99 negative items that the user did not interact with; we then evaluated how well the model ranked the test item against the negative ones.
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We did this because there was no target-domain ground truth for users in \(U^\textrm{cold}\).
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References
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, pp. 2787–2795 (2013)
Chiang, W., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257–266 (2019)
Gao, C., et al.: Cross-domain recommendation without sharing user-relevant data. In: Proceedings of the 30th International Conference on World Wide Web, pp. 491–502 (2019)
He, R., Kang, W., McAuley, J.: Translation-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems, pp. 161–169 (2017)
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)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Hu, G., Zhang, Y., Yang, Q.: CoNet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 667–676 (2018)
Kang, S., Hwang, J., Lee, D., Yu, H.: Semi-supervised learning for cross-domain recommendation to cold-start users. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1563–1572 (2019)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Kula, M.: Metadata embeddings for user and item cold-start recommendations. In: Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Recommender Systems, pp. 14–21 (2015)
Li, P., Tuzhilin, A.: DDTCDR: deep dual transfer cross domain recommendation. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 331–339 (2020)
Liu, M., Li, J., Li, G., Pan, P.: Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 885–894 (2020)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2464–2470 (2017)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Proceedings of the 21th International Conference on Neural Information Processing Systems, pp. 1257–1264 (2007)
Pan, W., Liu, N.N., Xiang, E.W., Yang, Q.: Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 2318–2323 (2011)
Pan, W., Xiang, E., Liu, N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. vol. 24, pp. 230–235 (2010)
Recht, B., Re, C., Wright, S., Niu, F.: Hogwild!: a lock-free approach to parallelizing stochastic gradient descent. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, vol. 24, pp. 693–701 (2011)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian Personalized Ranking from Implicit Feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)
Wang, J., Lv, J.: Tag-informed collaborative topic modeling for cross domain recommendations. Knowl. Based Syst. 203, 106119 (2020)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 165–174 (2019)
Zhang, Q., Hao, P., Lu, J., Zhang, G.: Cross-domain recommendation with semantic correlation in tagging systems. In: 2019 International Joint Conference on Neural Networks, pp. 1–8 (2019)
Zhao, C., Li, C., Xiao, R., Deng, H., Sun, A.: CATN: cross-domain recommendation for cold-start users via aspect transfer network. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 229–238 (2020)
Zhu, F., Wang, Y., Chen, C., Zhou, J., Li, L., Liu, G.: Cross-domain Recommendation: Challenges, Progress, and Prospects. arXiv preprint arXiv:2103.01696 (2021)
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Huang, YT. et al. (2023). CPR: Cross-Domain Preference Ranking with User Transformation. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_35
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DOI: https://doi.org/10.1007/978-3-031-28238-6_35
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