Shuai Zhang
ABSTRACT
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is that it explicitly models user interests as a hypercuboid instead of a point in the space. In our approach, the recommendation score is learned by calculating a compositional distance between the user hypercuboid and the item. This helps to alleviate the potential geometric inflexibility of existing collaborative filtering approaches, enabling a greater extent of modeling capability. Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests. A neural architecture is also proposed to facilitate user hypercuboid learning by capturing the activity sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of our proposed model via extensive experiments on both public and commercial datasets. Empirical results show that our approach achieves very promising results, outperforming existing state-of-the-art.
- 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 TKDE, Vol. 17, 6 (2005), 734--749.Google Scholar
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Recsys. 191--198.Google Scholar
- John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. JMLR, Vol. 12, Jul (2011), 2121--2159.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning .MIT press.Google ScholarDigital Library
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017a. Translation-based recommendation. In Recsys. 161--169.Google Scholar
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017b. Translation-Based Recommendation. In Recsys (Como, Italy) (RecSys '17). Association for Computing Machinery, New York, NY, USA, 161--169. https://doi.org/10.1145/3109859.3109882Google Scholar
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507--517.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017c. Neural collaborative filtering. In WWW. 173--182.Google Scholar
- 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
- Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Recsys. 241--248.Google Scholar
- Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In WWW. 193--201.Google Scholar
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In ICDM. Ieee, 263--272.Google Scholar
- Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, and Changsheng Xu. 2018. Csan: Contextual self-attention network for user sequential recommendation. In MM. 447--455.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. IEEE, 197--206.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD. 426--434.Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 8 (2009), 30--37.Google ScholarDigital Library
- Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, and Andrew McCallum. 2018. Smoothing the Geometry of Probabilistic Box Embeddings. In ICLR.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. 2635--2643.Google ScholarDigital Library
- Chen Ma, Peng Kang, and Xue Liu. 2019. Hierarchical gating networks for sequential recommendation. In SIGKDD. 825--833.Google Scholar
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR, Vol. 9, Nov (2008), 2579--2605.Google Scholar
- Pradipta Maji. 2012. A rough hypercuboid approach for feature selection in approximation spaces. TKDE, Vol. 26, 1 (2012), 16--29.Google Scholar
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR. 43--52.Google Scholar
- Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. Comput. Surveys, Vol. 51, 4 (2018), 1--36.Google ScholarDigital Library
- Hongyu Ren*, Weihua Hu*, and Jure Leskovec. 2020. Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings. In ICLR.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI. AUAI Press, Arlington, Virginia, USA, 452--461.Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1--35.Google Scholar
- 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
- Guy Shani and Asela Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook. Springer, 257--297.Google Scholar
- Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. Comput. Surveys, Vol. 47, 1 (2014), 1--45.Google ScholarDigital Library
- Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. JMLR, Vol. 15, 1 (2014), 1929--1958.Google ScholarDigital Library
- Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009).Google Scholar
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 17--22.Google ScholarDigital Library
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565--573.Google Scholar
- Yi Tay, Luu Anh Tuan, and Siu Cheung Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In WWW. 729--739.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. 5998--6008.Google Scholar
- John Venn. 1880. I. On the diagrammatic and mechanical representation of propositions and reasonings. The London, Edinburgh, and Dublin philosophical magazine and journal of science, Vol. 10, 59 ( 1880), 1--18.Google Scholar
- Luke Vilnis, Xiang Li, Shikhar Murty, and Andrew McCallum. 2018. Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 263--272. https://doi.org/10.18653/v1/P18--1025Google ScholarCross Ref
- Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, and Xiaoli Li. 2020. HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems. In WSDM. 609--617.Google Scholar
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative Deep Learning for Recommender Systems. In SIGKDD (Sydney, NSW, Australia) (KDD '15). Association for Computing Machinery, New York, NY, USA, 1235--1244. https://doi.org/10.1145/2783258.2783273Google Scholar
- J. Wei, S. Wang, and X. Yuan. 2010. Ensemble Rough Hypercuboid Approach for Classifying Cancers. TKDE, Vol. 22, 3 (March 2010), 381--391.Google Scholar
- Jason Weston, Ron J Weiss, and Hector Yee. 2013. Nonlinear latent factorization by embedding multiple user interests. In Recsys. ACM, 65--68.Google Scholar
- Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In WSDM. 153--162.Google Scholar
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems.. In IJCAI. 3203--3209.Google Scholar
- Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2019. A simple convolutional generative network for next item recommendation. In WSDM. 582--590.Google Scholar
- Aston Zhang, Zachary C Lipton, Mu Li, and Alexander J Smola. 2019 a. Dive into Deep Learning. Unpublished draft. Retrieved, Vol. 3 (2019), 319.Google Scholar
- Hao Zhang and Lynne E Parker. 2011. 4-dimensional local spatio-temporal features for human activity recognition. In 2011 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2044--2049.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 b. Deep Learning Based Recommender System: A Survey and New Perspectives. Comput. Surveys, Vol. 52, 1, Article 5 (Feb. 2019). https://doi.org/10.1145/3285029Google Scholar
- Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, and Yi Tay. 2019 c. Quaternion collaborative filtering for recommendation. In IJCAI. AAAI Press, 4313--4319.Google Scholar
- Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018. Atrank: An attention-based user behavior modeling framework for recommendation. In AAAI.Google Scholar
Index Terms
- Learning User Representations with Hypercuboids for Recommender Systems
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
User Personality and User Satisfaction with Recommender Systems
In this study, we show that individual users' preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingOver the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
Comments