Abstract
Product recommenders based on users’ interests are becoming increasingly essential in e-commerce. With the continuous development of the recommendation system, the available information is further enriched. In the case, user’s click or purchase behavior could be a visual representation of his or her interest. Due to the rapid update of products, users’ interests are not static, but change over time. In order to cope with the users’ interest changes, we propose a desirable work on the basis of representative recommendation algorithm. The sequence of user interaction behavior is thoroughly utilized, and the items that users interact at different times have different significance for the reflect of users’ interests. By considering the user’s sequential behaviors, this paper focuses on the recent ones to obtains the real interest of user. In this process, user behavior is divided into long-term and short-term, modeled by LSTM and Attention-based model respectively for user’s next click recommendation. We refer this model as LANCR and analyze the model in experiment. The experiment demonstrates that the proposed model has superior improvement compared with standard approaches. We deploy our model on two real datasets to verify the superior performance made in predicting user preferences.
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
Liu, D.R., Lai, C.H., Lee, W.J.: A hybrid of sequential rules and collaborative filtering for product recommendation. Inf. Sci. 179(20), 3505–3519 (2009)
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. ACM (2010)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573. ACM (2018)
Zhou, C., et al.: ATRank: an attention-based user behavior modeling framework for recommendation. In: Proceeding of the 32nd AAAI Conference on Artificial Intelligence (2018)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Ni, Y., et al.: Perceive your users in depth: learning universal user representations from multiple e-commerce tasks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 596–605. ACM (2018)
Villatel, K., Smirnova, E., Mary, J., et al.: Recurrent neural networks for long and short-term sequential recommendation. arXiv preprint arXiv:1807.09142 (2018)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. arXiv preprint arXiv:1811.00855 (2018)
Zhang, S., Tay, Y., Yao, L., Sun, A.: Next item recommendation with self-attention. arXiv preprint arXiv:1808.06414 (2018)
Bai, T., Du, P., Zhao, W.X., Wen, J.R., Nie, J.Y.: A long-short demands-aware model for next-item recommendation. arXiv preprint arXiv:1903.00066 (2019)
Li, Z., Zhao, H., Liu, Q., Huang, Z., Mei, T., Chen, E.: Learning from history and present: next-item recommendation via discriminatively exploiting user behaviors. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1734–1743. ACM (2018)
Chung, J., Ahn, S., Bengio, Y.: Hierarchical multiscale recurrent neural networks. arXiv preprint arXiv:1609.01704 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, S., Zhang, J., Zong, C.: Learning sentence representation with guidance of human attention. arXiv preprint arXiv:1609.09189 (2016)
Yang, Z., He, X., Gao, J., Deng, L., Smola, A.: Stacked attention networks for image question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21–29 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000. IEEE (2010)
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, pp. 452–461. AUAI Press (2009)
Gilks, W.R., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC, Boca Raton (1995)
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. ACM (2016)
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. ACM (2018)
Pu, C., Wu, Z., Chen, H., Xu, K., Cao, J.: A sequential recommendation for mobile apps: what will user click next app?. In: 2018 IEEE International Conference on Web Services (ICWS), pp. 243–248. IEEE (2018)
Donkers, T., Loepp, B., Ziegler, J.: Sequential user-based recurrent neural network recommendations. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 152–160. ACM (2017)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)
Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based transactional context embedding for next-item recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Acknowledgements
This work was supported in part by National Natural Science Foundation of China under grants 61876069 and 61572226, and Jilin Province Key Scientific and Technological Research and Development project under grants 20180201067GX and 20180201044GX.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, X., Zuo, X., Yang, B. (2019). Sequential Recommendation Based on Long-Term and Short-Term User Behavior with Self-attention. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-29551-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29550-9
Online ISBN: 978-3-030-29551-6
eBook Packages: Computer ScienceComputer Science (R0)