ABSTRACT
Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial applications, such as recommendations and online advertising. How to precisely capture users' dynamic and evolving interests from previous interactions (e.g., clicks, purchases, etc.) is a challenging task in CTR prediction. Mainstream approaches focus on disentangling user interests in a heuristic way or modeling user interests into a static representation. However, these approaches overlook the importance of users' current intent and the complex interactions between their current intent and global interests. To address these concerns, in this paper, we propose a novel intent-enhanced user interest modeling for click-through rate prediction in large-scale e-commerce recommendations, abbreviated as IUI. Methodologically, different from existing works, we consider users' recent interactions to be inspired by their implicit intent and then leverage an intent-aware network to model their current local interests in a more precise and fine-grained manner. In addition, to obtain a more stable co-dependent global and local interest representation, we employ a co-attention network capable of activating the corresponding interest in global-level interactions and capturing the dynamic interactions between global- and local-level interaction behaviors. Finally, we incorporate self-supervised learning into the model training by maximizing the mutual information between the global and local representations obtained via the above two networks to enhance the CTR prediction performance. Compared with existing methods, IUI benefits from the different granularity of user interest to generate a more accurate and comprehensive preference representation. Experimental results demonstrate that the proposed model outperforms previous state-of-the-art methods in various metrics on three real-world datasets. In addition, an online A/B test deployed on the JD recommendation platforms shows a promising improvement across multiple evaluation metrics.
- Icek Ajzen. 2002. Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality and social psychology review, Vol. 6, 2 (2002), 107--122.Google Scholar
- Dolores Albarracin and Robert S Wyer Jr. 2000. The cognitive impact of past behavior: influences on beliefs, attitudes, and future behavioral decisions. Journal of personality and social psychology, Vol. 79, 1 (2000), 5.Google ScholarCross Ref
- Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021. Sequential recommendation with graph neural networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 378--387.Google ScholarDigital Library
- Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior sequence transformer for e-commerce recommendation in alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. 1--4.Google ScholarDigital Library
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7--10.Google ScholarDigital Library
- Zhifang Fan, Dan Ou, Yulong Gu, Bairan Fu, Xiang Li, Wentian Bao, Xin-Yu Dai, Xiaoyi Zeng, Tao Zhuang, and Qingwen Liu. 2022. Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 262--270.Google ScholarDigital Library
- 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. arXiv preprint arXiv:1905.06482 (2019).Google Scholar
- Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, and Zhe Wang. 2017. Learning piece-wise linear models from large scale data for ad click prediction. arXiv preprint arXiv:1704.05194 (2017).Google Scholar
- Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2020. Graph enhanced representation learning for news recommendation. In Proceedings of The Web Conference 2020. 2863--2869.Google ScholarDigital Library
- Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. 2010. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine. Omnipress.Google ScholarDigital Library
- Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, and Dawei Yin. 2020. Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2493--2500.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. arXiv preprint arXiv:1703.04247 (2017).Google Scholar
- Li He, Hongxu Chen, Dingxian Wang, Shoaib Jameel, Philip Yu, and Guandong Xu. 2021. Click-Through Rate Prediction with Multi-Modal Hypergraphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 690--699.Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. 507--517.Google ScholarDigital Library
- Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM conference on recommender systems. 43--50.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197--206.Google ScholarCross Ref
- Jiacheng Li, Yujie Wang, and Julian McAuley. 2020a. Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th international conference on web search and data mining. 322--330.Google ScholarDigital Library
- Shihao Li, Dekun Yang, and Bufeng Zhang. 2020b. MRIF: Multi-resolution interest fusion for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1765--1768.Google ScholarDigital Library
- Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang. 2019. Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 539--548.Google ScholarDigital Library
- Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. 2019. Feature generation by convolutional neural network for click-through rate prediction. In The World Wide Web Conference. 1119--1129.Google ScholarDigital Library
- Feng Liu, Wei Guo, Huifeng Guo, Ruiming Tang, Yunming Ye, and Xiuqiang He. 2020. Dual-attentional factorization-machines based neural network for user response prediction. In Companion Proceedings of the Web Conference 2020. 26--27.Google ScholarDigital Library
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1831--1839.Google ScholarDigital Library
- Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu. 2020. Disentangled self-supervision in sequential recommenders. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 483--491.Google ScholarDigital Library
- Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, and Peilin Zhao. 2022. Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 353--362.Google ScholarDigital Library
- Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020. Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2685--2692.Google ScholarDigital Library
- Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995--1000.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web. 811--820.Google ScholarDigital Library
- Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web. 521--530.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 Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 255--262.Google ScholarDigital Library
- Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen, and Zhao Li. 2022a. Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation. In Proceedings of the ACM Web Conference 2022. 422--430.Google ScholarDigital Library
- Qijie Shen, Hong Wen, Jing Zhang, and Qi Rao. 2022b. Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1767--1776.Google ScholarDigital Library
- Si Shen, Botao Hu, Weizhu Chen, and Qiang Yang. 2012. Personalized click model through collaborative filtering. In Proceedings of the fifth ACM international conference on Web search and data mining. 323--332.Google ScholarDigital Library
- Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441--1450.Google ScholarDigital Library
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining. 565--573.Google ScholarDigital Library
- Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2022. CL4CTR: A Contrastive Learning Framework for CTR Prediction. arXiv preprint arXiv:2212.00522 (2022).Google Scholar
- Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2021. A survey on neural recommendation: From collaborative filtering to content and context enriched recommendation. arXiv preprint arXiv:2104.13030 (2021).Google Scholar
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 346--353.Google ScholarDigital Library
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617 (2017).Google Scholar
- 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 network. In IJCAI International Joint Conference on Artificial Intelligence.Google ScholarCross Ref
- Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, and Enhong Chen. 2021. Multi-interactive attention network for fine-grained feature learning in ctr prediction. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 984--992.Google ScholarDigital Library
- Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2022a. Disentangling Long and Short-Term Interests for Recommendation. In Proceedings of the ACM Web Conference 2022. 2256--2267.Google ScholarDigital Library
- Zuowu Zheng, Changwang Zhang, Xiaofeng Gao, and Guihai Chen. 2022b. HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction. arXiv preprint arXiv:2206.00510 (2022).Google Scholar
- 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 Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 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. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059--1068.Google ScholarDigital Library
Index Terms
- IUI: Intent-Enhanced User Interest Modeling for Click-Through Rate Prediction
Recommendations
Satisfaction-Aware User Interest Network for Click-Through Rate Prediction
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementClick-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate ...
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
WWW '22: Proceedings of the ACM Web Conference 2022In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper, we present a new recommendation problem, Trigger-...
MIN: multi-dimensional interest network for click-through rate prediction
AbstractClick-through rate (CTR) prediction is a critical task in recommender systems and online advertising systems. The extensive collection of behavior data has become popular for building prediction models by capturing user interests from behavior ...
Comments