Abstract
Although cross-domain recommender systems (CDRSs) are promising approaches to solving the cold-start problem, most CDRSs require overlapped users, which significantly limits their applications. To remove the overlap limitation, researchers introduced domain adversarial learning and embedding attribution alignment to develop non-overlapped CDRSs. Existing non-overlapped CDRSs, however, have several drawbacks. They ignore the semantic relations between source and target items, leading to noisy knowledge transfer. Moreover, they learn knowledge from both domain-shared and domain-specific preferences and are hence easily misled by the source-domain-specific preferences. To overcome these drawbacks, we propose a novel semantic relation-based knowledge transfer framework (SRTrans). We semantically cluster the source and the target items and calculate their similarities to extract relational knowledge between domains. To transfer the relational knowledge, we develop a new two-tier graph transfer network. Last, we introduce a task-oriented knowledge distillation supervision and combine it with a prediction loss to alleviate the negative impact of the source-domain-specific preferences. Our experimental results on real-world datasets demonstrate that SRTrans significantly outperforms state-of-the-art models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
grouplens.org/datasets/movielens/25m/.
- 2.
jmcauley.ucsd.edu/data/amazon/.
- 3.
- 4.
References
Chen, Z., Xiao, R., Li, C., Ye, G., Sun, H., Deng, H.: ESAM: discriminative domain adaptation with non-displayed items to improve long-tail performance. In: SIGIR, pp. 579–588 (2020)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171–4186 (2019)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation. In: SIGIR, pp. 639–648 (2020)
Kang, S., Hwang, J., Kweon, W., Yu, H.: DE-RRD: A knowledge distillation framework for recommender system. In: CIKM, pp. 605–614 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Krishnan, A., Das, M., Bendre, M., Yang, H., Sundaram, H.: Transfer learning via contextual invariants for one-to-many cross-domain recommendation. In: SIGIR, pp. 1081–1090 (2020)
Li, Z., et al.: Debiasing graph transfer learning via item semantic clustering for cross-domain recommendations. In: IEEE Big Data, pp. 762–769 (2022)
Li, Z., Amagata, D., Zhang, Y., Maekawa, T., Hara, T., Yonekawa, K., Kurokawa, M.: Hml4rec: hierarchical meta-learning for cold-start recommendation in flash sale e-commerce. Knowl.-Based Syst. 255, 109674 (2022)
Liu, M., Li, J., Li, G., Pan, P.: Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In: CIKM, pp. 885–894 (2020)
Liu, W., Zheng, X., Hu, M., Chen, C.: Collaborative filtering with attribution alignment for review-based non-overlapped cross domain recommendation. In: Web Conference, pp. 1181–1190 (2022)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: An embedding and mapping approach. In: Sierra, C. (ed.) IJCAI, pp. 2464–2470 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)
Wang, C., Niepert, M., Li, H.: Recsys-dan: discriminative adversarial networks for cross-domain recommender systems. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 2731–2740 (2020)
Wang, H., et al.: Preliminary investigation of alleviating user cold-start problem in e-commerce with deep cross-domain recommender system. In: ECNLP, pp. 398–403 (2019)
Wang, H., et al.: A dnn-based cross-domain recommender system for alleviating cold-start problem in e-commerce. IEEE Open J. Indust. Electron. Society 1, 194–206 (2020)
Zhu, F., Wang, Y., Chen, C., Liu, G., Zheng, X.: A graphical and attentional framework for dual-target cross-domain recommendation. In: IJCAI, pp. 3001–3008 (2020)
Zhu, Y., et al..: Personalized transfer of user preferences for cross-domain recommendation. In: WSDM, pp. 1507–1515 (2022)
Acknowledgement
This work partially supported by JST CREST Grant Number JPMJCR21F2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Z. et al. (2023). Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations. 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_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-33380-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33379-8
Online ISBN: 978-3-031-33380-4
eBook Packages: Computer ScienceComputer Science (R0)