ABSTRACT
Recommender Systems have been playing essential roles in e-commerce portals. Existing recommendation algorithms usually learn the ranking scores of items by optimizing a single task (e.g. Click-through rate prediction) based on users' historical click sequences, but they generally pay few attention to simultaneously modeling users' multiple types of behaviors or jointly optimize multiple objectives (e.g. both Click-through rate and Conversion rate), which are both vital for e-commerce sites. In this paper, we argue that it is crucial to formulate users' different interests based on multiple types of behaviors and perform multi-task learning for significant improvement in multiple objectives simultaneously. We propose Deep Multifaceted Transformers (DMT), a novel framework that can model users' multiple types of behavior sequences simultaneously with multiple Transformers. It utilizes Multi-gate Mixture-of-Experts to optimize multiple objectives. Besides, it exploits unbiased learning to reduce the selection bias in the training data. Experiments on JD real production dataset demonstrate the effectiveness of DMT, which significantly outperforms state-of-art methods. DMT has been successfully deployed to serve the main traffic in the commercial Recommender System in JD.com. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/CIKM2020_DMT.
Supplemental Material
- Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, and Thorsten Joachims. 2019. A General Framework for Counterfactual Learning-to-Rank. In SIGIR'19. 5--14.Google ScholarDigital Library
- Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H Chi. 2018. Latent cross: Making use of context in recurrent recommender systems. In WSDM'18. 46--54.Google ScholarDigital Library
- Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019 b. Behavior sequence transformer for e-commerce recommendation in Alibaba. In DLP-KDD'19. 1--4.Google Scholar
- Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, and Yongdong Zhang. 2019 a. Semi-supervised user profiling with heterogeneous graph attention networks. In IJCAI'19. 2116--2122.Google ScholarCross Ref
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et almbox. 2016. Wide & deep learning for recommender systems. In DLRS'16. 7--10.Google ScholarDigital Library
- Aleksandr Chuklin, Pavel Serdyukov, and Maarten De Rijke. 2013. Click model-based information retrieval metrics. SIGIR'13. 493--502.Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. RecSys'16. 191--198.Google Scholar
- Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. In IJCAI'19. 2301--2307.Google ScholarCross Ref
- Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.Google Scholar
- Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020. Hierarchical User Profiling for E-commerce Recommender Systems. In WSDM'20. 223--231.Google Scholar
- Yulong Gu, Jiaxing Song, Weidong Liu, and Lixin Zou. 2016. HLGPS: a home location global positioning system in location-based social networks. In ICDM'16. 901--906.Google ScholarCross Ref
- 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. 1725--1731.Google ScholarCross Ref
- Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et almbox. 2014. Practical lessons from predicting clicks on ads at facebook. In ADKDD'14. 1--9.Google Scholar
- Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased learning-to-rank with biased feedback. In WSDM'17. 781--789.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM'18. 197--206.Google Scholar
- Chenyi Lei, Shouling Ji, and Zhao Li. 2019. TiSSA: A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors. In WWW'19. 2964--2970.Google Scholar
- Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019. Multi-interest network with dynamic routing for recommendation at Tmall. In CIKM'19.Google ScholarDigital Library
- Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In WSDM'20. 322--330.Google Scholar
- Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, and Peng Jiang. 2019. A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In RecSys'19. 20--28.Google Scholar
- 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 Scholar
- Yiding Liu, Yulong Gu, Zhuoye Ding, Junchao Gao, Ziyi Guo, Yongjun Bao, and Weipeng Yan. 2020. Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items. In CIKM'20.Google Scholar
- Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Why I like it: multi-task learning for recommendation and explanation. In RecSys'18. 4--12.Google Scholar
- Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018b. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In KDD'18. 1930--1939.Google Scholar
- Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018a. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In SIGIR'18. 1137--1140.Google ScholarDigital Library
- Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring networks of substitutable and complementary products. In KDD'15. 785--794.Google ScholarDigital Library
- Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, and Yanlong Du. 2019. Deep spatio-temporal neural networks for click-through rate prediction. In KDD'19. 2078--2086.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
- Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao. 2016. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In KDD'16. 255--262.Google ScholarDigital Library
- Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. Autoint: Automatic feature interaction learning via self-attentive neural networks. In CIKM'19. 1161--1170.Google ScholarDigital Library
- 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
- Huizhao Wang, Guanfeng Liu, Yan Zhao, Bolong Zheng, Pengpeng Zhao, and Kai Zheng. 2019. DMFP: A Dynamic Multi-faceted Fine-Grained Preference Model for Recommendation. In ICDM'19. 608--617.Google Scholar
- Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale commodity embedding for e-commerce recommendation in alibaba. In KDD'18. 839--848.Google ScholarDigital Library
- Shanfeng Wang, Maoguo Gong, Haoliang Li, and Junwei Yang. 2016b. Multi-objective optimization for long tail recommendation. Knowledge-Based Systems (2016), 145--155.Google Scholar
- Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016a. Learning to rank with selection bias in personal search. In SIGIR'16. 115--124.Google ScholarDigital Library
- Dawei Yin, Yuening Hu, Jiliang Tang, Tim Daly, Mianwei Zhou, Hua Ouyang, Jianhui Chen, Changsung Kang, Hongbo Deng, Chikashi Nobata, et almbox. 2016. Ranking relevance in yahoo search. In KDD'16. 323--332.Google Scholar
- 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. 974--983.Google ScholarDigital Library
- Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai, Chuck Rosenburg, and Jure Leskovec. 2019. Hierarchical temporal convolutional networks for dynamic recommender systems. In WWW'19. 2236--2246.Google ScholarDigital Library
- Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In RecSys'19. 43--51.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, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In AAAI'19. 5941--5948.Google ScholarDigital Library
- 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. 1059--1068.Google ScholarDigital Library
- Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019. Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems. In KDD'19. 2810--2818.Google ScholarDigital Library
- Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Huang, and Dawei Yin. 2020. Neural Interactive Collaborative Filtering. In SIGIR'20. 749--758.Google Scholar
Index Terms
- Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems
Recommendations
Deep Multifaceted Highlight Network for Multi-objective Ranking in Trigger-Induced Recommendation
CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of ThingsRecommender Systems have been proven to be of great business value in e-commerce platforms and recommendation algorithms play an important role in it. E-commerce platforms provide entrances for customers to enter channels that can meet their specific ...
Recommender systems for product bundling
Recommender systems (RS) are a class of information filter applications whose main goal is to provide personalized recommendations, content, and services to users. Recommendation services may support a firm's marketing strategy and contribute to ...
A survey of recommender systems with multi-objective optimization
AbstractRecommender systems have been widely applied to several domains and applications to assist decision making by recommending items tailored to user preferences. One of the popular recommendation algorithms is the model-based approach ...
Comments