Abstract
Recent advances in cross-domain recommendation have shown great potential in improving sample efficiency and coping with the challenge of data sparsity via transferring the knowledge from the source domain to the target domain. Previous cross-domain recommendation methods are generally based on extracting information from overlapping users, which limits their performance when there are not sufficient overlapping users. In this paper, we propose a contrastive-based cross-domain recommendation framework for cold-start users that simultaneously transfers knowledge about overlapping users and user-item interactions to optimize the user/item representations. To this end, two contrastive loss functions and two specific learning tasks are proposed. The proposed framework can make fuller use of the information on the source domain and reduce the demand for overlapping users, while maintaining or even enhancing recommendation performance. Experimental results on a real-world dataset demonstrate the efficacy and effectiveness of our framework on the top-N cross-domain recommendation task.
This research was funded by the Shenzhen Science and Technology Innovation Commission (JCYJ20210324135011030, WDZC20200818121348001), Guangdong Pearl River Plan (2019QN01X890), and National Natural Science Foundation of China (Grant No. 71971127).
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References
Yue, X., et al.: Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13834–13844 (2021)
Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., Huang, Z.: From zero-shot learning to cold-start recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4189–4196 (2019)
Wu, T., Chio, E.K.I., Cheng, H.T., Du, Y.: Zero-shot heterogeneous transfer learning from recommender systems to cold-start search retrieval. In: Proceedings of the Conference on Information & Knowledge Management, pp. 2821–2828 (2010)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008)
Mirbakhsh, N., Ling, C.X.: Improving top-n recommendation for cold-start users via cross-domain information. In: ACM Transactions on Knowledge Discovery from Data (TKDD), pp. 1–19 (2015)
Gao, C., et al.: Cross-domain recommendation without sharing user-relevant data. In: The World Wide Web Conference, pp. 491–502 (2019)
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)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2464–2470 (2017)
Fu, W., Peng, Z., Wang, S., Xu, Y., Li, J.: Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 94–101 (2019)
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)
Wang, B., Zhang, C., Zhang, H., Lyu, X., Tang, Z.: dual autoencoder network with swap reconstruction for cold-start recommendation. In: Proceedings of the Conference on Information and Knowledge Management, pp. 2249–2252 (2020)
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)
Ni, J., Li, J., McAuley, J.: justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), pp. 188–197 (2019)
Zhu, Y., et al.: Transfer-meta framework for cross-domain recommendation to cold-start users. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1813–1817 (2021)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020)
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: collaborative filtering recommender systems. In: The Adaptive Web, pp. 291–324 (2007)
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, pp. 452–461 (2009)
Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative Metric Learning. In: The World Wide Web Conference, pp. 193–201 (2017)
He, J., Liu, R., Fuzhen, Z., Fen, L., Qing, H.: A general cross-domain recommendation framework via bayesian neural network. In: IEEE International Conference on Data Mining (ICDM) (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: The World Wide Web Conference, pp. 173–182 (2017)
Zhu, F., Wang, Y., Chen, C., Liu, G., Zheng, X.: A graphical and attentional framework for dual-target cross-domain recommendation. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3001–3008 (2020)
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Chen, Y., Yao, Y., Chan, W.K.V. (2023). CLCDR: Contrastive Learning for Cross-Domain Recommendation to Cold-Start Users. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_28
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