Abstract
Session-based recommendation shows increasing importance in E-commerce, news and multimedia applications. Its main challenge is to predict next item just using a short anonymous behavior sequence. Some works introduce other close similar sessions as complementary to help recommendation. But users’ online behaviors are diverse and very similar sessions are always rare, so the information provided by such similar sessions is limited. In fact, if we observe the data at the high level of coarse granularity, we will find that they may present certain regularity of content and patterns. The selection of close neighborhood sessions at tag level can solve the problem of data sparsity and improve the quality of recommendation. Therefore, we propose a novel model CoKnow that is a collaborative knowledge-aware session-based recommendation model. In this model, we establish a tag-based neighbor selection mechanism. Specifically, CoKnow contains two modules: Current session modeling with item tag(Cu-tag) and Neighbor session modeling with item tag (Ne-tag). In Cu-tag, we construct an item graph and a tag graph based on current session, and use graph neural networks to learn the representations of items and tags. In Ne-tag, a memory matrix is used to store the representations of neighborhood sessions with tag information, and then we integrate these representations according to their similarity with current session to get the output. Finally, the outputs of these two modules are combined to obtain the final representation of session for recommendation. Extensive experiments on real-world datasets show that our proposed model outperforms other state-of-the-art methods consistently.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web (2001)
Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6(12/1/2005), 1265–1295 (2005)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: International Conference on Learning Representations 2016 (ICLR) (2016)
JLi, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., De Rijke, M. : RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4806–4813 (2019)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T. : Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 346–353 (2019)
Liu, L., Wang, L., Lian, T.: CaSe4SR: using category sequence graph to augment session-based recommendation. Knowl. Based Syst. 212, 106558 (2021)
Weston, J., Chopra, S., Bordes, A.: Memory network. In: International Conference on Learning Representations 2015 (ICLR) (2015)
Wang, M., Ren, P., Mei, L., Chen, Z., Ma, J., de Rijke, M. : A collaborative session-based recommendation approach with parallel memory modules. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345–354 (2019)
Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: SIGIR 2004 (2004)
Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: collaborative denoising auto-encoders for top-n recommender systems. In: WSDM 2016 (2016)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web (2017)
Qiao, J., Wang, L., Duan, L.: Sequence and graph structure co-awareness via gating mechanism and self-attention for session-based recommendation. Int. J. Mach. Learn. Cybern. 12(9), 2591–2605 (2021). https://doi.org/10.1007/s13042-021-01343-3
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, pp. 3940–3946 (2019)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 579–588 (2019)
Wang, W., et al.: Beyond clicks: modeling multi-relational item graph for session-based target behavior prediction. In: Proceedings of The Web Conference 2020 (2020)
Chen, X., et al.: Sequential recommendation with user memory networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 108–116 (2018)
Huang, J., Zhao, W.X., Dou, H., Wen, J.R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 505–514 (2018)
Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22 (2016)
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, pp. 811–820 (2010)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp. 843–852 (2018)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018)
Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 582–590 (2019)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61872260), particularly supported by Science and Technology Innovation Project of Higher Education Institutions in Shanxi Province (No. 2020L0102).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, L., Wang, L., Lian, T. (2021). Discovering Proper Neighbors to Improve Session-Based Recommendation. In: Oliver, N., PĂ©rez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12975. Springer, Cham. https://doi.org/10.1007/978-3-030-86486-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-86486-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86485-9
Online ISBN: 978-3-030-86486-6
eBook Packages: Computer ScienceComputer Science (R0)