A Dynamic Co-attention Network for Session-based Recommendation

ABSTRACT

Session-based recommendation is the task of recommending the next item a user might be interested in given partially known session information, e.g., part of a session or recent historical sessions. An effective session-based recommender should be able to exploit a user's evolving preferences, which we assume to be a mixture of her short- and long-term interests. Existing session-based recommendation methods often embed a user's long-term preference into a static representation, which plays a fixed role when dealing with her current short-term interests. This is problematic because long-term preferences may be more or less important for predicting the next conversion depending on the user's short-term interests. We propose a DCN-SR. DCN-SR applies a co-attention network to capture the dynamic interactions between the user's long- and short-term interaction behavior and generates co-dependent representations of the user's long- and short-term interests. For modeling a user's short-term interaction behavior, we design a CGRU network to take actions like "click'', "collect'' and "buy'' into account. Experiments on e-commerce datasets show significant improvements of DCN-SR over state-of-the-art session-based recommendation methods, with improvements of up to 2.58% on the Tmall dataset and 3.08% on the Tianchi dataset in terms of Recall@10. MRR@10 improvements are 3.78% and 4.05%, respectively. We also investigate the scalability and sensitivity of DCN-SR. The improvements of DCN-SR over state-of-the-art baselines are especially noticeable for short sessions and active users with many historical interactions.

References

1. Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowledge and Data Engineering. , Vol. 17, 6 (2005), 734--749.
2. Alexey Borisov, Martijn Wardenaar, Ilya Markov, and Maarten de Rijke. 2018. A Click Sequence Model for Web Search. In SIGIR '18. ACM, 45--54.
3. Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2018a. Attention-based Hierarchical Neural Query Suggestion. In SIGIR '18 . ACM, 1093--1096.
4. Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018b. Sequential Recommendation with User Memory Networks. In WSDM '18. ACM, 108--116.
5. Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017. Sequential User-based Recurrent Neural Network Recommendations. In RecSys '17 . ACM, 152--160.
6. Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized Ranking Metric Embedding for Next New POI Recommendation. In IJCAI'15 . AAAI Press, 2069--2075.
7. Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional Sequence to Sequence Learning. CoRR , Vol. abs/1705.03122 (2017).
8. Ruining He and Julian McAuley. 2016. Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In International Conference on Data Mining. IEEE, 191--200.
9. Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. TriRank: Review-aware Explainable Recommendation by Modeling Aspects. In CIKM '15 . ACM, 1661--1670.
10. Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018a. Adversarial Personalized Ranking for Recommendation. In SIGIR '18 . ACM, 355--364.
11. Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yugang Jiang, and Tatseng Chua. 2018b. NAIS: Neural Attentive Item Similarity Model for Recommendation. IEEE Trans. Knowledge and Data Engineering , Vol. 30, 12 (2018), 2354--2366.
12. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW '17. ACM, 173--182.
13. Balá zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In ICLR'16. 1--10.
14. Katja Hofmann, Anne Schuth, Shimon Whiteson, and Maarten de Rijke. 2013. Reusing Historical Interaction Data for Faster Online Learning to Rank for Information Retrieval. In WSDM '13. ACM, 183--192.
15. Xue Hong-Jian, Dai Xinyu, Zhang Jianbing, Huang Shujian, and Chen Jiajun. 2017. Deep Matrix Factorization Models for Recommender Systems. In IJCAI '17 . 3203--3209.
16. Dietmar Jannach. 2018. Keynote: Session-Based Recommendation -- Challenges and Recent Advances. In KI 2018: Advances in Artificial Intelligence. Springer International Publishing, Cham, 3--7.
17. Diederik Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014).
18. Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In KDD '08. ACM, 426--434.
19. Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural Attentive Session-based Recommendation. In CIKM '17. ACM, 1419--1428.
20. Qiang Liu, Shu Wu, and Liang Wang. 2017. Multi-Behavioral Sequential Prediction with Recurrent Log-Bilinear Model. IEEE Trans. Knowledge and Data Engineering , Vol. 29, 6 (2017), 1254--1267.
21. Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation. In KDD '18 . ACM, 1831--1839.
22. Chen Ma, Yingxue Zhang, Qinglong Wang, and Xue Liu. 2018. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. In CIKM '18. ACM , 697--706.
23. Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. In RecSys '17. ACM, 130--137.
24. Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation. In AAAI '19. AAAI.
25. Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing Personalized Markov Chains for Next-basket Recommendation. In WWW '10 . ACM, 811--820.
26. Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. In DLRS 2016 . ACM, 17--22.
27. Jiaxi Tang and Ke Wang. 2018. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In WSDM '18 . ACM, 565--573.
28. Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018a. Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking. In WWW '18 . International World Wide Web Conferences Steering Committee, 729--739.
29. Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018b. Multi-Pointer Co-Attention Networks for Recommendation. CoRR , Vol. abs/1801.09251 (2018).
30. Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2018. Mix 'n match: Integrating Text Matching and Product Substitutability within Product Search. In CIKM '18. ACM, 1373--1382.
31. Mengting Wan and Julian McAuley. 2018. Item Recommendation on Monotonic Behavior Chains. In RecSys '18. ACM.
32. Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning Hierarchical Representation Model for NextBasket Recommendation. In SIGIR '15 . ACM, 403--412.
33. Shoujin Wang, Longbing Cao, and Yan Wang. 2019. A Survey on Session-based Recommender Systems. CoRR , Vol. abs/1902.04864 (2019).
34. Bo Wu, Wen Huang Cheng, Yongdong Zhang, Huang Qiushi, Li Jintao, and Tao Mei. 2017. Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks. In IJCAI '17. 3062--3068.
35. Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential Recommender System based on Hierarchical Attention Networks. In IJCAI'18 . 3926--3932.
36. Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. A Dynamic Recurrent Model for Next Basket Recommendation. In SIGIR '16 . ACM, 729--732.
37. Shuai Zhang, Lina Yao, and Aixin Sun. 2017. Deep Learning based Recommender System: A Survey and New Perspectives. arXiv preprint arXiv:1707.07435 (2017).
38. Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, and Manqing Dong. 2018. NeuRec: On Nonlinear Transformation for Personalized Ranking. arXiv preprint arXiv:1805.03002 (2018).
39. Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In WSDM '17 . ACM, 425--434.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems