Abstract
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed. Traditional CDR leverages the relatively richer information from a richer domain to improve recommendation performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users between domains, where they extract the embeddings of common users and then transfer the embeddings to the two target domains to improve recommendation performance. However, in real life, the proportion of common users between domains is usually very small, which makes it hard to generate representative and high-quality user embeddings, and thus, limits the performance of the existing methods in real applications. To address this problem, in this paper, we propose a Three-Layer Attentional Framework based on Similar Users, called TASU. In addition to common users, TASU leverages information from similar users to improve the quality of user embeddings. By a three-layer attentional framework, TASU can generate more representative and high-quality user embeddings to improve recommendation performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that TASU significantly outperforms the state-of-the-art approaches.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 355–359. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73078-1_44
Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., Musial, K.: Multi-level graph convolutional networks for cross-platform anchor link prediction. In: SIGKDD, pp. 1503–1511 (2020)
Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.S.: Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: SIGIR, pp. 335–344. ACM (2017)
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: SIGKDD, pp. 855–864. ACM (2016)
Guo, L., Tang, L., Chen, T., Zhu, L., Nguyen, Q.V.H., Yin, H.: Da-gcn: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv preprint arXiv:2105.03300 (2021)
Guo, L., Zhang, J., Chen, T., Wang, X., Yin, H.: Reinforcement learning-enhanced shared-account cross-domain sequential recommendation. In: TKDE (2022)
He, X., Chen, T., Kan, M.Y., Chen, X.: Trirank: Review-aware explainable recommendation by modeling aspects. In: CIKM, pp. 1661–1670. ACM (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)
He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558. ACM (2016)
Hu, G., Zhang, Y., Yang, Q.: Conet: Collaborative cross networks for cross-domain recommendation. In: CIKM, pp. 667–676. ACM (2018)
Kang, S., Hwang, J., Lee, D., Yu, H.: Semi-supervised learning for cross-domain recommendation to cold-start users. In: CIKM, pp. 1563–1572. ACM (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196. PMLR (2014)
Li, P., Tuzhilin, A.: Ddtcdr: Deep dual transfer cross domain recommendation. In: WSDM, pp. 331–339 (2020)
Liu, G., Wang, Y., Orgun, M.A.: Trust transitivity in complex social networks. In: AAAI (2011)
Liu, G., Wang, Y., Orgun, M.A., Lim, E.P.: Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks. IEEE T Serv Comput. 6(2), 152–167 (2011)
Man, T., Shen, H., Jin, X., Cheng, X.: Cross-domain recommendation: An embedding and mapping approach. In: IJCAI, vol. 17, pp. 2464–2470 (2017)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: ACL System Demonstrations, pp. 55–60 (2014)
Mihalcea, R., Corley, C., Strapparava, C., et al.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI, vol. 6, pp. 775–780 (2006)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007 (1995)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Wen, P., Yuan, W., Qin, Q., Sang, S., Zhang, Z.: Neural attention model for recommendation based on factorization machines. Appl. Intell. 51(4), 1829–1844 (2021)
Xue, H.J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, pp. 3203–3209 (2017)
Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T.S.: Discrete collaborative filtering. In: SIGIR, pp. 325–334. ACM (2016)
Zhang, Q., Liao, W., Zhang, G., Yuan, B., Lu, J.: A deep dual adversarial network for cross-domain recommendation. In: TKDE (2021)
Zhao, C., Li, C., Xiao, R., Deng, H., Sun, A.: Catn: Cross-domain recommendation for cold-start users via aspect transfer network. In: SIGIR, pp. 229–238. ACM (2020)
Zhu, F., Chen, C., Wang, Y., Liu, G., Zheng, X.: Dtcdr: A framework for dual-target cross-domain recommendation. In: CIKM, pp. 1533–1542. ACM (2019)
Zhu, F., Wang, Y., Chen, C., Liu, G., Orgun, M., Wu, J.: A deep framework for cross-domain and cross-system recommendations. arXiv preprint arXiv:2009.06215 (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, 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.: Transfer-meta framework for cross-domain recommendation to cold-start users. In: SIGIR, pp. 1813–1817. ACM (2021)
Zhu, Y., et al.: Personalized transfer of user preferences for cross-domain recommendation. In: WSDM, pp. 1507–1515 (2022)
Acknowledgements
This work was supported by Shanghai Science and Technology Commission (No. 22YF1401100), Fundamental Research Funds for the Central Universities (No. 22D111210, 22D111207), and National Science Fund for Young Scholars (No. 62202095).
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Lu, J., Sun, G., Fang, X., Yang, J., He, W. (2023). A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_20
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