Yukuo Cen
ABSTRACT
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items that the user might be interacted with. Recent works usually give an overall embedding from a user's behavior sequence. However, a unified user embedding cannot reflect the user's multiple interests during a period. In this paper, we propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity. We conduct experiments for the sequential recommendation on two real-world datasets, Amazon and Taobao. Experimental results demonstrate that our framework achieves significant improvements over state-of-the-art models. Our framework has also been successfully deployed on the offline Alibaba distributed cloud platform.
Supplemental Material
- Gediminas Adomavicius and YoungOk Kwon. 2011. Improving aggregate recommendation diversity using ranking-based techniques. TKDE, Vol. 24, 5 (2011), 896--911.Google Scholar
Digital Library
- Sujoy Bag, Abhijeet Ghadge, and Manoj Kumar Tiwari. 2019. An integrated recommender system for improved accuracy and aggregate diversity. Computers & Industrial Engineering, Vol. 130 (2019), 187--197.Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- Keith Bradley and Barry Smyth. 2001. Improving recommendation diversity. In AICS'01. Citeseer, 85--94.Google Scholar
- Peter J Burt. 1988. Attention mechanisms for vision in a dynamic world. In ICPR'88. IEEE, 977--987.Google ScholarCross Ref
- Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In KDD'19. 1358--1368.Google ScholarDigital Library
- Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In WSDM'18. ACM, 108--116.Google ScholarDigital Library
- Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In WWW'17. 183--192.Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys'16. ACM, 191--198.Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Tommaso Di Noia, Vito Claudio Ostuni, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio. 2014. An analysis of users' propensity toward diversity in recommendations. In RecSys'14. 285--288.Google Scholar
- Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In SIGIR'18. ACM, 515--524.Google ScholarDigital Library
- Anupriya Gogna and Angshul Majumdar. 2017. Balancing accuracy and diversity in recommendations using matrix completion framework. Knowledge-Based Systems, Vol. 125 (2017), 83--95.Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In IJCAI'17.Google ScholarCross Ref
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS'17. 1024--1034.Google ScholarDigital Library
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based recommendation. In RecSys'17. ACM, 161--169.Google Scholar
- Ruining He and Julian McAuley. 2016a. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM'16. IEEE, 191--200.Google ScholarCross Ref
- Ruining He and Julian McAuley. 2016b. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW'16. International World Wide Web Conferences Steering Committee, 507--517.Google ScholarDigital Library
- Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In SIGIR'17. ACM, 355--364.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural collaborative filtering. In WWW'17. International World Wide Web Conferences Steering Committee, 173--182.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR'16.Google Scholar
- Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang. 2011. Transforming auto-encoders. In ICANN'11. Springer, 44--51.Google ScholarCross Ref
- Kalervo Järvelin and Jaana Kekäläinen. 2000. IR evaluation methods for retrieving highly relevant documents. In SIGIR'00. ACM, 41--48.Google ScholarDigital Library
- Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015. On using very large target vocabulary for neural machine translation. ACL'15.Google ScholarCross Ref
- Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017).Google Scholar
- M Kalaivanan and K Vengatesan. 2013. Recommendation system based on statistical analysis of ranking from user. In ICICES'13. IEEE, 479--484.Google ScholarCross Ref
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM'18. IEEE, 197--206.Google ScholarCross Ref
- George Karypis. 2001. Evaluation of item-based top-n recommendation algorithms. In CIKM'01. ACM, 247--254.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 8 (2009), 30--37.Google ScholarDigital Library
- Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019 a. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. arXiv preprint arXiv:1904.08030 (2019).Google Scholar
- Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019 b. A Capsule Network for Recommendation and Explaining What You Like and Dislike. In SIGIR'19. ACM, 275--284.Google ScholarDigital Library
- Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018. xDeepFM: Combining explicit and implicit feature interactions for recommender systems. In KDD'18. ACM, 1754--1763.Google ScholarDigital Library
- Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW'18. 689--698.Google ScholarDigital Library
- Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. In ICLR'17.Google Scholar
- Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, and Wilfred Ng. 2019. SDM: Sequential deep matching model for online large-scale recommender system. In CIKM'19. 2635--2643.Google ScholarDigital Library
- Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. In NIPS'19. 5712--5723.Google Scholar
- Benjamin Marlin. 2004. Collaborative filtering: A machine learning perspective. University of Toronto Toronto.Google Scholar
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR'15. ACM, 43--52.Google ScholarDigital Library
- Katja Niemann and Martin Wolpers. 2013. A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In KDD'13. 955--963.Google ScholarDigital Library
- Umberto Panniello, Alexander Tuzhilin, and Michele Gorgoglione. 2014. Comparing context-aware recommender systems in terms of accuracy and diversity. UMUAI, Vol. 24, 1--2 (2014), 35--65.Google ScholarDigital Library
- Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Practice on long sequential user behavior modeling for click-through rate prediction. In KDD'19. 2671--2679.Google ScholarDigital Library
- Lijing Qin and Xiaoyan Zhu. 2013. Promoting diversity in recommendation by entropy regularizer. In IJCAI'13.Google Scholar
- Steffen Rendle. 2010. Factorization machines. In ICDM'10. IEEE, 995--1000.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW'10. ACM, 811--820.Google ScholarDigital Library
- Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton. 2017. Dynamic routing between capsules. In NIPS'17. 3856--3866.Google Scholar
- Badrul Munir Sarwar, George Karypis, Joseph A Konstan, John Riedl, et almbox. 2001. Item-based collaborative filtering recommendation algorithms. WWW'01 (2001), 285--295.Google Scholar
- J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The adaptive web. Springer, 291--324.Google Scholar
- Malcolm Slaney and William White. 2006. Measuring playlist diversity for recommendation systems. In AMCMM'06 workshop. 77--82.Google ScholarDigital Library
- Yaoru Sun and Robert Fisher. 2003. Object-based visual attention for computer vision. Artificial intelligence, Vol. 146, 1 (2003), 77--123.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS'17. 5998--6008.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In ADKDD'17. ACM, 12.Google ScholarDigital Library
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017. Recurrent recommender networks. In WSDM'17. ACM, 495--503.Google ScholarDigital Library
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems.. In IJCAI'17. 3203--3209.Google ScholarCross Ref
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD'18. ACM, 974--983.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Ting Yu, Junpeng Guo, Wenhua Li, Harry Jiannan Wang, and Ling Fan. 2019. Recommendation with diversity: An adaptive trust-aware model. Decision Support Systems, Vol. 123 (2019), 113073.Google ScholarDigital Library
- Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018a. Atrank: An attention-based user behavior modeling framework for recommendation. In AAAI'18.Google ScholarCross Ref
- Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018b. Deep interest network for click-through rate prediction. In KDD'18. ACM, 1059--1068.Google ScholarDigital Library
- Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In KDD'18. ACM, 1079--1088.Google ScholarDigital Library
Index Terms
- Controllable Multi-Interest Framework for Recommendation
Recommendations
GIMIRec: Global Interaction-aware Multi-Interest framework for sequential Recommendation
AbstractSequential recommendation based on multi-interest framework is to model the user’s recent interaction sequence into multiple different interest vectors instead of a single low-dimensional vector, so as to fully represent the diversity of user ...
Improving Sequential Recommendation with Attribute-Augmented Graph Neural Networks
Advances in Knowledge Discovery and Data MiningAbstractMany practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-...
Multi-interest Diversification for End-to-end Sequential Recommendation
Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often ...
Comments