Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks

ABSTRACT

Sequential history of user interactions as well as the context of interactions provide valuable information to recommender systems, for modeling user behavior. Modeling both contexts and sequential information simultaneously, in context-aware sequential recommenders, has been shown to outperform methods that model either one of the two aspects. In long sequential histories, temporal trends are also found within sequences of contexts and temporal gaps that are not modeled by previous methods. In this paper we design new context-aware sequential recommendation methods, based on Stacked Recurrent Neural Networks, that model the dynamics of contexts and temporal gaps. Experiments on two large benchmark datasets demonstrate the advantages of modeling the evolution of contexts and temporal gaps - our models significantly outperform state-of-the-art context-aware sequential recommender systems.

References

1. Charu C Aggarwal. 2016. Recommender Systems. Springer.
2. Shuo Chen, Josh L Moore, Douglas Turnbull, and Thorsten Joachims. 2012. Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 714-722.
3. Salah El Hihi and Yoshua Bengio. 1996. Hierarchical recurrent neural networks for long-term dependencies. In Proceedings of the 8th International Conference on Neural Information Processing Systems (NIPS). 493-499.
4. Li Gao, Jia Wu, Chuan Zhou, and Yue Hu. 2017. Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation.. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 1316-1322.
5. F Maxwell Harper and Joseph A Konstan. 2016. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TIIS)5, 4(2016), 19.
6. Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web. 507-517.
7. Baoxing Huai, Enhong Chen, Hengshu Zhu, Hui Xiong, Tengfei Bao, Qi Liu, and Jilei Tian. 2014. Toward personalized context recognition for mobile users: A semisupervised Bayesian HMM approach. ACM Transactions on Knowledge Discovery from Data (TKDD)9, 2(2014), 10.
8. Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM Conference on Recommender Systems. ACM, 79-86.
9. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations (ICLR).
10. Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang. 2016. Context-aware sequential recommendation. In Proceedings of the 16th International Conference on Data Mining (ICDM), 2016 IEEE. IEEE, 1053-1058.
11. Qiang Liu, Shu Wu, and Liang Wang. 2015. COT: Contextual Operating Tensor for Context-Aware Recommender Systems.. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 203-209.
12. Cosimo Palmisano, Alexander Tuzhilin, and Michele Gorgoglione. 2008. Using context to improve predictive modeling of customers in personalization applications. IEEE Transactions on Knowledge and Data Engineering (TKDE)20, 11(2008), 1535-1549.
13. Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2013. How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026(2013).
14. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452-461.
15. Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, 811-820.
16. Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 635-644.
17. David E Rumelhart and James L McClelland. 1986. Parallel distributed processing: explorations in the microstructure of cognition. volume 1. foundations. MIT Press, Cambridge, MA.
18. Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, and Alan Hanjalic. 2014. Cars2: Learning context-aware representations for context-aware recommendations. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM). ACM, 291-300.
19. Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 650-658.
20. Yu Sun, Nicholas Jing Yuan, Yingzi Wang, Xing Xie, Kieran McDonald, and Rui Zhang. 2016. Contextual intent tracking for personal assistants. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 273-282.
21. Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, and Rui Zhang. 2016. Collaborative nowcasting for contextual recommendation. In Proceedings of the 25th International Conference on World Wide Web. 1407-1418.
22. Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 403-412.
23. Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.
24. Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A dynamic recurrent model for next basket recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 729-732.