Sequential Recommendation with User Memory Networks

ABSTRACT

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users» historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users» sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users» future actions are affected by previous behaviors.

References

1. S. Chen, J. L. Moore, D. Turnbull, and T. Joachims. Playlist prediction via metric embedding. In KDD, 2012.
2. X. Chen, Z. Qin, Y. Zhang, and T. Xu. Learning to rank features for recommendation over multiple categories. In SIGIR, 2016.
3. C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: Successive point-of-interest recommendation. In IJCAI, 2013.
4. S. Feng, X. Li, Y. Zeng, G. Cong, Y. M. Chee, and Q. Yuan. Personalized ranking metric embedding for next new poi recommendation. In IJCAI, 2015.
5. A. Graves, G. Wayne, and I. Danihelka. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014.
6. A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwi'nska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, et al. Hybrid computing using a neural network with dynamic external memory. 2016.
7. E. Grefenstette, K. M. Hermann, M. Suleyman, and P. Blunsom. Learning to transduce with unbounded memory. In NIPS, 2015.
8. N. Hariri, B. Mobasher, and R. Burke. Context-aware music recommendation based on latent topic sequential patterns. In RecSys, 2012.
9. R. He, W.-C. Kang, and J. McAuley. Translation-based recommendation. Recsys, 2017.
10. R. He and J. McAuley. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM, 2016.
11. R. He and J. McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW, 2016.
12. X. He, T. Chen, M.-Y. Kan, and X. Chen. Trirank: Review-aware explainable recommendation by modeling aspects. In CIKM, 2015.
13. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. Neural collaborative filtering. In WWW, 2017.
14. B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. Session-based recommendations with recurrent neural networks. ICLR, 2016.
15. K. J"arvelin and J. Kek"al"ainen. Ir evaluation methods for retrieving highly relevant documents. In SIGIR, 2000.
16. G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM, 2001.
17. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.
18. A. Kumar, O. Irsoy, P. Ondruska, M. Iyyer, J. Bradbury, I. Gulrajani, V. Zhong, R. Paulus, and R. Socher. Ask me anything: Dynamic memory networks for natural language processing. In ICML, 2016.
19. Q. Liu, S. Wu, D. Wang, Z. Li, and L. Wang. Context-aware sequential recommendation. In ICDM, 2016.
20. J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel. Image-based recommendations on styles and substitutes. In SIGIR, 2015.
21. A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston. Key-value memory networks for directly reading documents. EMNLP, 2016.
22. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.
23. S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW, 2010.
24. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. Meta-learning with memory-augmented neural networks. In ICML, 2016.
25. S. Sukhbaatar, J. Weston, R. Fergus, et al. End-to-end memory networks. In NIPS, 2015.
26. Y. K. Tan, X. Xu, and Y. Liu. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on DLRS, 2016.
27. P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng. Learning hierarchical representation model for nextbasket recommendation. In SIGIR, 2015.
28. J. Weston, S. Chopra, and A. Bordes. Memory networks. ICLR, 2015.
29. X. Wu, Q. Liu, E. Chen, L. He, J. Lv, C. Cao, and G. Hu. Personalized next-song recommendation in online karaokes. In Recsys, 2013.
30. L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via long-and short-term preference fusion. In KDD, 2010.
31. F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan. A dynamic recurrent model for next basket recommendation. In SIGIR, 2016.
32. J. Zhang, X. Shi, I. King, and D.-Y. Yeung. Dynamic key-value memory networks for knowledge tracing. In WWW, 2017.
33. Y. Zhang. Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation. In WSDM, 2015.
34. Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In SIGIR, 2014.
35. Y. Zhang, M. Zhang, Y. Liu, C. Tat-Seng, Y. Zhang, and S. Ma. Task-based recommendation on a web-scale. In BigData, 2015.

Index Terms

• Information systems

  • Information systems applications

    • Data mining

      • Collaborative filtering

  • World Wide Web

    • Web searching and information discovery

      • Collaborative filtering

      • Content ranking

      • Personalization