ABSTRACT
In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.
- Oren Barkan and Noam Koenigstein . 2016. Item2vec: neural item embedding for collaborative filtering Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on. IEEE, 1--6.Google Scholar
- Robert M Bell and Yehuda Koren . 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on. IEEE, 43--52. Google ScholarDigital Library
- Yoshua Bengio, Aaron Courville, and Pascal Vincent . 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828. Google ScholarDigital Library
- Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang, and Lifeng Sun . 2011. Who should share what?: item-level social influence prediction for users and posts ranking Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 185--194. Google ScholarDigital Library
- Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu . 2017. A Survey on Network Embedding. arXiv preprint arXiv:1711.08752 (2017).Google Scholar
- Tim Donkers, Benedikt Loepp, and Jürgen Ziegler . 2017. Sequential User-based Recurrent Neural Network Recommendations Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys '17). ACM, New York, NY, USA, 152--160. Google ScholarDigital Library
- John Duchi, Elad Hazan, and Yoram Singer . 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research Vol. 12, Jul (2011), 2121--2159. Google ScholarDigital Library
- Ali Mamdouh Elkahky, Yang Song, and Xiaodong He . 2015. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems Proceedings of the 24th International Conference on World Wide Web (WWW '15). 278--288. Google ScholarDigital Library
- Shalini Ghosh, Oriol Vinyals, Brian Strope, Scott Roy, Tom Dean, and Larry Heck . 2016. Contextual lstm (clstm) models for large scale nlp tasks. arXiv preprint arXiv:1602.06291 (2016).Google Scholar
- Aditya Grover and Jure Leskovec . 2016. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855--864. 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 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17. 1725--1731. Google ScholarDigital Library
- Ruining He and Julian McAuley . 2016. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. AAAI. 144--150. Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua . 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). 173--182. Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua . 2016. Fast matrix factorization for online recommendation with implicit feedback Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 549--558. Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk . 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber . 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Yehuda Koren . 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426--434. Google ScholarDigital Library
- Yehuda Koren . 2010. Collaborative filtering with temporal dynamics. Commun. ACM Vol. 53, 4 (2010), 89--97. Google ScholarDigital Library
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma . 2017. Neural Attentive Session-based Recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17). 1419--1428. Google ScholarDigital Library
- Greg Linden, Brent Smith, and Jeremy York . 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing Vol. 7, 1 (2003), 76--80. Google ScholarDigital Library
- Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, and Hui Xiong . 2011. Personalized travel package recommendation. In Data Mining (ICDM), 2011 IEEE 11th International Conference on. IEEE, 407--416. Google ScholarDigital Library
- Qi Liu, Xianyu Zeng, Hengshu Zhu, Enhong Chen, Hui Xiong, Xing Xie, et almbox. . 2015. Mining indecisiveness in customer behaviors. In Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 281--290. Google ScholarDigital Library
- Laurens van der Maaten and Geoffrey Hinton . 2008. Visualizing data using t-SNE. Journal of Machine Learning Research Vol. 9, Nov (2008), 2579--2605.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean . 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119. Google ScholarDigital Library
- Andriy Mnih and Ruslan R Salakhutdinov . 2008. Probabilistic matrix factorization. In Advances in neural information processing systems. 1257--1264. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena . 2014. Deepwalk: Online learning of social representations Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 701--710. Google ScholarDigital Library
- Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi . 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys '17). 130--137. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme . 2009. BPR: Bayesian personalized ranking from implicit feedback Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452--461. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme . 2010. Factorizing personalized markov chains for next-basket recommendation Proceedings of the 19th international conference on World wide web. ACM, 811--820. Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B Kantor . 2015. Recommender systems handbook. Springer. Google Scholar
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl . 2001. Item-based collaborative filtering recommendation algorithms Proceedings of the 10th international conference on World Wide Web. ACM, 285--295. Google ScholarDigital Library
- Mike Schuster and Kuldip K Paliwal . 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing Vol. 45, 11 (1997), 2673--2681. Google ScholarDigital Library
- Shuo Shang, Ruogu Ding, Bo Yuan, Kexin Xie, Kai Zheng, and Panos Kalnis . 2012. User Oriented Trajectory Search for Trip Recommendation Proceedings of the 15th International Conference on Extending Database Technology (EDBT '12). ACM, New York, NY, USA, 156--167. Google ScholarDigital Library
- Shuo Shang, Ruogu Ding, Kai Zheng, Christian S Jensen, Panos Kalnis, and Xiaofang Zhou . 2014. Personalized trajectory matching in spatial networks. The VLDB Journal, Vol. 23, 3 (2014), 449--468. Google ScholarDigital Library
- Jun Wang and Qiang Tang . 2016. A probabilistic view of neighborhood-based recommendation methods Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on. IEEE, 14--20.Google Scholar
- Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng . 2015. Learning Hierarchical Representation Model for NextBasket Recommendation Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 403--412. Google ScholarDigital Library
- Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, and Tao Mei . 2017. Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17. 3062--3068. Google ScholarDigital Library
- Bo Wu, Tao Mei, Wen-Huang Cheng, Yongdong Zhang, et almbox. . 2016. Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition.. In AAAI. 272--278. Google ScholarDigital Library
- Ghim-Eng Yap, Xiao-Li Li, and Philip Yu . 2012. Effective next-items recommendation via personalized sequential pattern mining Database Systems for Advanced Applications. Springer Berlin/Heidelberg, 48--64. Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma . 2016. Collaborative knowledge base embedding for recommender systems Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 353--362. Google ScholarDigital Library
- Hongke Zhao, Qi Liu, Yong Ge, Ruoyan Kong, and Enhong Chen . 2016. Group Preference Aggregation: A Nash Equilibrium Approach Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 679--688.Google Scholar
- Hongke Zhao, Qi Liu, Hengshu Zhu, Yong Ge, Enhong Chen, Yan Zhu, and Junping Du . 2017. A sequential approach to market state modeling and analysis in online p2p lending. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2017).Google Scholar
- Hongke Zhao, Le Wu, Qi Liu, Yong Ge, and Enhong Chen . 2014. Investment recommendation in p2p lending: A portfolio perspective with risk management Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 1109--1114. Google ScholarDigital Library
- Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
Recommendations
What Can History Tell Us?
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementRecommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential ...
Solving the Sparsity Problem in Recommendations via Cross-Domain Item Embedding Based on Co-Clustering
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data MiningSession-based recommendations recently receive much attentions due to no available user data in many cases, e.g., users are not logged-in/tracked. Most session-based methods focus on exploring abundant historical records of anonymous users but ignoring ...
A Probabilistic Model for the Cold-Start Problem in Rating Prediction Using Click Data
Neural Information ProcessingAbstractOne of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers ...
Comments