Abstract
Due to the anonymity of user sessions, most existing session-based recommender systems (SBRSs) cannot effectively learn user features, leading to failure to make personalized recommendations. Besides, these SBRSs may neglect some similar items with common features if they are long-distance in the session graphs or global graphs. In this paper, we propose a novel SBRS based on heterogeneous graph neural network, which can effectively learn user and item embeddings for personalized recommendations. Furthermore, we find out the user and item informative anchors in the heterogeneous graph and propagate their features in the same type of nodes, which can help to explore those long-distance but similar items. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate the effectiveness of our proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barabási, A.L.: Linked: the new science of networks. Am. J. Phys. 71(4), 409–410 (2003)
Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 1172–1180. KDD 2020, Association for Computing Machinery, New York, USA (2020)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 57–66. KDD 2001, Association for Computing Machinery, New York, USA (2001)
Forsati, R., Meybodi, M., Neiat, A.G.: Web page personalization based on weighted association rules. In: Proceedings of the International Conference on Electronic Computer Technology, pp. 130–135 (2009)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations (ICLR), Conference Track Proceedings. San Juan, Puerto Rico (2016)
Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems (RecSys), pp. 306–310. RecSys 2017, Association for Computing Machinery, New York, USA (2017)
Jomsri, P.: Book recommendation system for digital library based on user profiles by using association rule. In: Proceedings of the 4th edition of the International Conference on the Innovative Computing Technology (INTECH), pp. 130–134 (2014)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Latifi, S., Jannach, D.: Streaming session-based recommendation: when graph neural networks meet the neighborhood. In: Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), pp. 420–426. RecSys 2022, Association for Computing Machinery, New York, USA (2022)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM), pp. 1419–1428. CIKM 2017, Association for Computing Machinery, New York, USA (2017)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Pang, Y., et al.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM), pp. 775–783. WSDM 2022, Association for Computing Machinery, New York, USA (2022)
Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the 11th ACM Conference on Recommender Systems (RecSys), pp. 130–137. RecSys 2017, Association for Computing Machinery, New York, USA (2017)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 811–820. WWW 2010, Association for Computing Machinery, New York, USA (2010)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW), pp. 285–295. ACM, Hong Kong, China (2001)
Tang, J., Wang, K.: Personalized Top-N sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pp. 565–573. WSDM 2018, Association for Computing Machinery, New York, USA (2018)
Wang, N., Wang, S., Wang, Y., Sheng, Q.Z., Orgun, M.: Modelling local and global dependencies for next-item recommendations. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2020. LNCS, vol. 12343, pp. 285–300. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62008-0_20
Wang, Z., Wei, W., Cong, G., Li, X.L., Mao, X.L., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 169–178. SIGIR 2020, Association for Computing Machinery, New York, USA (2020)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)
Xu, C., et al.: Recurrent convolutional neural network for sequential recommendation. In: Proceedings of the 28th International Conference on World Wide Web (WWW), pp. 3398–3404. WWW 2019, Association for Computing Machinery, New York, USA (2019)
Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., Wang, L.: Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Trans. Knowl. Data Eng. 34(8), 3946–3957 (2022)
Zhang, Z., Nasraoui, O.: Efficient hybrid web recommendations based on Markov clickstream models and implicit search. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 621–627 (2007)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant U1811263 and the Science and Technology Program of Guangzhou under Grant 2023A04J1728.
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 Singapore Pte Ltd.
About this paper
Cite this paper
Lin, R., Teng, L., Tang, F., Zhong, H., Yuan, C., Mao, C. (2023). Informative Anchor-Enhanced Heterogeneous Global Graph Neural Networks for Personalized Session-Based Recommendation. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-7254-8_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7253-1
Online ISBN: 978-981-99-7254-8
eBook Packages: Computer ScienceComputer Science (R0)