Abstract
Personalized recommendation refers to identifying items that satisfy users’ interests from large-scale item databases according to users’ habits and preferences. The task is very challenging due to the complexity of user interests. Previous works use a uniform representation to model user interests, neglecting the diversity of user preferences when they adopt items. However, users consider many different attributes when choosing an item. Introducing attribute-level matching information into the model can express user interests more accurately. To achieve this goal, we propose a novel Attribute-level Interest Matching Network (AIMN) for personalized recommendation. We first adopt a knowledge representation learning method to construct spaces of different attributes, then employ a knowledge graph to extend entities as side information for representing users. Finally, we project entities and candidate items into diverse attribute spaces, match and aggregate them to realize fine-grained attribute-level information matching. Empirical results demonstrate that the proposed AIMN achieves substantial gains on several benchmarks, beating many solid baselines and achieving state-of-art performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ai, Q., Azizi, V., Chen, X., Zhang, Y.: Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11(9), 137 (2018)
Berg, R.V.D., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. In: KDD (2017)
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
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 (2017)
Guo, Q., et al.: A survey on knowledge graph-based recommender systems. CoRR (2020)
He, X., He, Z., Song, J., Liu, Z., Jiang, Y., Chua, T.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. In: The 26th World Wide Web Conference, pp. 173–182 (2017)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659–667 (2013)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Li, C., et al.: Multi-interest network with dynamic routing for recommendation at Tmall. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2615–2623 (2019)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)
Shi, G., Feng, C., Xu, W., Liao, L., Huang, H.: Penalized multiple distribution selection method for imbalanced data classification. Knowl. Based Syst. 1–9 (2020)
Tewari, A.S.: Generating items recommendations by fusing content and user-item based collaborative filtering. Procedia Comput. Sci. 167, 1934–1940 (2020)
Wang, H., et al.: Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417–426 (2018)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation, pp. 950–958. ACM (2019)
Wang, X., He, X., Nie, L., Chua, T.: Item silk road: recommending items from information domains to social users. In: Proceedings of the 40th International ACM SIGIR Conference, 7–11 August 2017, pp. 185–194 (2017)
Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.: Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR Conference, 25–30 July 2020, pp. 1001–1010 (2020)
Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inf. Syst. 37(3), 33 (2019)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM Conference, 19–23 August 2018, pp. 974–983 (2018)
Yuan, F., He, X., Karatzoglou, A., Zhang, L.: Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference, 25–30 July 2020, pp. 1469–1478 (2020)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: The 22nd ACM SIGKDD International Conference, pp. 353–362 (2016)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference, pp. 1059–1068 (2017)
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 61702022, No. 61802011, No. 61976010, Beijing Municipal Education Committee Science Foundation under Grant No. KM201910005024, Inner Mongolia Autonomous Region Science and Technology Foundation under Grant NO. 2021GG0333, and Beijing Postdoctoral Research Foundation under Grant No. Q6042001202101.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, R., Jian, M., Shi, G., Wu, L., Xiang, Y. (2021). Attribute-Level Interest Matching Network for Personalized Recommendation. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-88007-1_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88006-4
Online ISBN: 978-3-030-88007-1
eBook Packages: Computer ScienceComputer Science (R0)