Abstract
The problem of data sparsity is a key challenge for recommendation systems. It motivates the research of cross-domain recommendation (CDR), which aims to use more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, finding useful features to transfer is a challenge for CDR. Avoiding negative transfer while achieving domain adaptation further adds to this challenge. Based on the superiority of graph structural feature learning, this paper proposes a graph convolutional network based Cross-Domain Adaptive Recommendation model using Feature Constraints Learning (CDAR-FCL). To begin with, we construct a multi-graph network consisting of single-domain graphs and one cross-domain graph based on overlapping users. Next, we employ specific and common graph convolution on the graphs to learn domain-specific and domain-invariant features, respectively. Additionally, we design feature constraints on the features obtained in different graphs and mine the potential correlation for domain adaptation. To address the issue of shared parameter conflicts within the constraints, we develop a binary mask learning approach based on contrastive learning. Experiments on three pairs of real cross-domain datasets demonstrate the effectiveness.
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References
Cao, J., Lin, X., Cong, X., Ya, J., Liu, T., Wang, B.: Disencdr: learning disentangled representations for cross-domain recommendation. In: ACM SIGIR.,pp. 267–277 (2022)
Crawshaw, M.: Multi-task learning with deep neural networks: a survey. arXiv preprint arXiv:2009.09796 (2020)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: ACM SIGIR, pp. 639–648 (2020)
Hu, G., Zhang, Y., Yang, Q.: Conet: collaborative cross networks for cross-domain recommendation. In: ACM CIKM, pp. 667–676 (2018)
Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_11
Li, P., Tuzhilin, A.: Ddtcdr: deep dual transfer cross domain recommendation. In: ACM WSDM, pp. 331–339 (2020)
Liu, M., Li, J., Li, G., Pan, P.: Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In: ACM CIKM, pp. 885–894 (2020)
Liu, W., Su, J., Chen, C., Zheng, X.: Leveraging distribution alignment via stein path for cross-domain cold-start recommendation. Adv. Neural. Inf. Process. Syst. 34, 19223–19234 (2021)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: an embedding and mapping approach. In: IJCAI, vol. 17, pp. 2464–2470 (2017)
Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE T-NN 22(2), 199–210 (2010)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE TKDE 22(10), 1345–1359 (2010)
Steffen, R., Christoph, F., Zeno, G., Lars, S.T.: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press (2009)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: IEEE CVPR, pp. 7167–7176 (2017)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: ACM SIGIR, pp. 165–174 (2019)
Zhang, Y., Liu, Z., Ma, Y., Gao, Y.: Multi-graph convolutional feature transfer for cross-domain recommendation. In: IEEE IJCNN, pp. 1–8 (2022)
Zhao, C., Li, C., Fu, C.: Cross-domain recommendation via preference propagation graphnet. In: ACM CIKM, pp. 2165–2168 (2019)
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)
Zhu, Y., et al.: Personalized transfer of user preferences for cross-domain recommendation. In: ACM WSDM, pp. 1507–1515 (2022)
Acknowledgments
This work was supported by the National Key Research and Development Program of China(2019YFB2102500) and the National Natural Science Foundation of China (U2268203).
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Gao, Y., Liu, Z., Yang, X., Ding, Y., Lu, S., Ma, Y. (2024). Graph Convolutional Network Based Feature Constraints Learning for Cross-Domain Adaptive Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_12
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DOI: https://doi.org/10.1007/978-981-99-8178-6_12
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