Abstract
Graph neural networks have demonstrated impressive performance in the field of recommender systems. However, existing graph neural network recommendation approaches are proficient in capturing users’ mainstream interests and recommending popular items but fall short in effectively identifying users’ niche interests, thus failing to meet users’ personalized needs. To this end, this paper proposes the User-oriented Interest Representation on Knowledge Graph (UIR-KG) approach, which leverages the rich semantic information on the knowledge graph to learn users’ long-tail interest representation. UIR-KG maximizes the recommendation of long-tail items while satisfying users’ mainstream interests as much as possible. Firstly, a popular constraint-based long-tail neighbor selector is proposed, which obtains the target user’s long-tail neighbors by conducting high-order random walks on the collaborative knowledge graph and constraining item popularity. Secondly, a knowledge-enhanced hybrid attention aggregator is proposed, which conducts high-order aggregation of users’ long-tail interest representations on the collaborative knowledge graph by comprehensively combining relationship-aware attention and self-attention mechanisms. Finally, UIR-KG predicts the ratings of uninteracted items and provides Top-N recommendation results for the target user. Experimental results on real datasets show that compared with existing relevant approaches, UIR-KG can effectively improve recommendation diversity while maintaining recommendation accuracy, especially in long-tail recommendations. Code is available at https://github.com/ZZP-RS/UIR-KG
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Conference on Recommender Systems, RecSys 2008, New York, pp. 11–18. Association for Computing Machinery (2008). https://doi.org/10.1145/1454008.1454012
Zhang, Z., Kudo, Y., Murai, T., Ren, Y.: Improved covering-based collaborative filtering for new users’ personalized recommendations. Knowl. Inf. Syst. 62, 3133–3154 (2020). https://doi.org/10.1007/s10115-020-01455-2
Zhang, Z., Zhang, Y., Ren, Y.: Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering. Inf. Retrieval J. 23, 449–472 (2020). https://doi.org/10.1007/s10791-020-09378-w
Zhang, Z., Dong, M., Ota, K., Kudo, Y.: Alleviating new user cold-start in user-based collaborative filtering via bipartite network. IEEE Trans. Comput. Soc. Syst. 7(3), 672–685 (2020). https://doi.org/10.1109/TCSS.2020.2971942
Wang, Z., Lin, G., Tan, H., Chen, Q., Liu, X.: CKAN: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, New York, pp. 219–228. Association for Computing Machinery (2020). https://doi.org/10.1145/3397271.3401141
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, New York, pp. 950–958. Association for Computing Machinery (2019). https://doi.org/10.1145/3292500.3330989
Zhang, Z., Dong, M., Ota, K., Zhang, Y., Ren, Y.: LBCF: a link-based collaborative filtering for over-fitting problem in recommender system. IEEE Trans. Comput. Soc. Syst. 8(6), 1450–1464 (2021). https://doi.org/10.1109/TCSS.2021.3081424
Zhang, Y., Cheng, D.Z., Yao, T., Yi, X., Hong, L., Chi, E.H.: A model of two tales: dual transfer learning framework for improved long-tail item recommendation. In: Proceedings of the Web Conference 2021, WWW 2021, New York, NY, USA, pp. 2220–2231. Association for Computing Machinery (2021). https://doi.org/10.1145/3442381.3450086
Yin, H., Cui, B., Li, J., Yao, J., Chen, C.: Challenging the long tail recommendation. Proc. VLDB Endowment 5(9), 896–907 (2012). https://doi.org/10.14778/2311906.2311916
Zhang, Z., Dong, M., Ota, K., Zhang, Y., Kudo, Y.: Context-enhanced probabilistic diffusion for urban point-of-interest recommendation. IEEE Trans. Serv. Comput. 15(6), 3156–3169 (2022). https://doi.org/10.1109/TSC.2021.3085675
Wan, Q., He, X., Wang, X., Wu, J., Guo, W., Tang, R.: Cross pairwise ranking for unbiased item recommendation. In: Proceedings of The Web Conference 2022, WWW 2022, New York, pp. 2370–2378. Association for Computing Machinery (2022). https://doi.org/10.1145/3485447.3512010
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, UAI 2009, Arlington, Virginia, USA, pp. 452–461. AUAI Press (2009). https://doi.org/10.5555/1795114.1795167
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, New York, pp. 353–362. Association for Computing Machinery (2016) . https://doi.org/10.1145/2939672.2939673
Ai, Q., Azizi, V., Chen, X., Zhang, Y.: Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11(9), 137 (2018). https://doi.org/10.3390/a11090137
Acknowledgements
This work was supported in part by the National Science Foundation of China (No.61976109); Liaoning Revitalization Talents Program (No. XLYC2006005); The Scientific Research Project of Liaoning Province (No. LJKZ0963); Liaoning Province Ministry of Education (No. LJKQZ20222431); China Scholarship Council Foundation (No. 202108210173).
Author information
Authors and Affiliations
Corresponding authors
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
Zhang, Z., Zhang, Y., Wang, A., Zhou, P., Zhang, Y., Ren, Y. (2023). User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-46674-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46673-1
Online ISBN: 978-3-031-46674-8
eBook Packages: Computer ScienceComputer Science (R0)