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MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation

Published: 27 February 2023 Publication History

Abstract

Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.

Supplementary Material

MP4 File (WSDM23-fp0335.mp4)
Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. In this paper, we propose a new recommendation framework, MuSeNet, which is able to learn multiple implicit scenarios from the existing user behaviors. We perform extensive experiments to evaluate the effectiveness of MuSeNet and quantitative results show that the proposed method outperforms the competitors by up to 7.33% on average in terms of AUC.

References

[1]
Ashton Anderson, Ravi Kumar, Andrew Tomkins, and Sergei Vassilvitskii. 2014. The dynamics of repeat consumption. In WWW. 419--430.
[2]
Rahul Bhagat, Srevatsan Muralidharan, Alex Lobzhanidze, and Shankar Vishwanath. 2018. Buy it again: Modeling repeat purchase recommendations. In SIGKDD. 62--70.
[3]
Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui. 2020. Structural deep clustering network. In WWW. 1400--1410.
[4]
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018. Deep clustering for unsupervised learning of visual features. In ECCV. 132--149.
[5]
Jun Chen, Chaokun Wang, and Jianmin Wang. 2015. Will you" reconsume" the near past? fast prediction on short-term reconsumption behaviors. In AAAI.
[6]
Tianyi Chen, Yuejiao Sun, and Wotao Yin. 2021. Tighter analysis of alternating stochastic gradient method for stochastic nested problems. arXiv preprint arXiv:2106.13781 (2021).
[7]
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 Workshop on deep learning for recommender systems. 7--10.
[8]
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018. Measuring and mitigating unintended bias in text classification. In AAAI. 67--73.
[9]
Thomas George and Srujana Merugu. 2005. A scalable collaborative filtering framework based on co-clustering. In ICDM.
[10]
Madelyn Glymour, Judea Pearl, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons.
[11]
Songjie Gong. 2010. A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. Softw., Vol. 5, 7 (2010), 745--752.
[12]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. In IJCAI. 1725--1731.
[13]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.
[14]
Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical reparameterization with gumbel-softmax. In ICLR.
[15]
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. 2615--2623.
[16]
Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. 2021b. Prototypical contrastive learning of unsupervised representations. In ICLR.
[17]
Yu Li, Yi Zhang, Lu Gan, Gengwei Hong, Zimu Zhou, and Qiang Li. 2021a. RevMan: Revenue-aware multi-task online insurance recommendation. In AAAI.
[18]
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. 2013. Distance-based image classification: Generalizing to new classes at near-zero cost. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 11 (2013), 2624--2637.
[19]
Xichuan Niu, Bofang Li, Chenliang Li, Jun Tan, Rong Xiao, and Hongbo Deng. 2021. Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks. In WSDM. 1038--1046.
[20]
Judea Pearl. 2009. Causality. Cambridge university press.
[21]
Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten De Rijke. 2019. Repeatnet: A repeat aware neural recommendation machine for session-based recommendation. In AAAI. 4806--4813.
[22]
Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In RecSys. 240--248.
[23]
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. In SIGKDD. 1135--1144.
[24]
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning. PMLR, 1670--1679.
[25]
Qijie Shen, Wanjie Tao, Jing Zhang, Hong Wen, Zulong Chen, and Quan Lu. 2021. SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios. In CIKM. 4094--4103.
[26]
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 WWW. 422--430.
[27]
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 CIKM. 1767--1776.
[28]
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, et al. 2021. One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction. In CIKM. 4104--4113.
[29]
Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems, Vol. 30 (2017).
[30]
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. In CIKM. 1784--1793.
[31]
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In RecSys. 269--278.
[32]
Georgios Theocharous, Philip S. Thomas, and Mohammad Ghavamzadeh. 2015. Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees. In IJCAI.
[33]
Chenyang Wang. 2021. Towards Dynamic User Intention in Sequential Recommendation. In WSDM. 1121--1122.
[34]
Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2019. Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems. In WWW. 1977--1987.
[35]
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. In WWW. 1785--1797.
[36]
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In SIGKDD. 1791--1800.
[37]
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In MM. 1437--1445.
[38]
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. 2020. Noise or signal: The role of image backgrounds in object recognition. arXiv preprint arXiv:2006.09994 (2020).
[39]
Ling Yan, Wu-jun Li, Gui-Rong Xue, and Dingyi Han. 2014. Coupled group lasso for web-scale ctr prediction in display advertising. In ICML. 802--810.
[40]
Bo Yang, Xiao Fu, and Nicholas D Sidiropoulos. 2016. Learning from hidden traits: Joint factor analysis and latent clustering. IEEE Transactions on Signal Processing, Vol. 65, 1 (2016), 256--269.
[41]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. CSUR, Vol. 52, 1 (2019), 1--38.
[42]
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In SIGIR. 11--20.
[43]
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin. 2021. Disentangling user interest and conformity for recommendation with causal embedding. In WWW. 2980--2991.
[44]
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, Vol. 32.
[45]
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 SIGKDD. 1059--1068.
[46]
Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, and Kun Gai. 2017. Optimized cost per click in taobao display advertising. In SIGKDD. 2191--2200.

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  • (2024)Multimodal-aware Multi-intention Learning for RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681412(5663-5672)Online publication date: 28-Oct-2024
  • (2024)MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender SystemsProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635859(434-442)Online publication date: 4-Mar-2024
  • (2024)MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion ClassificationInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00489-617:1Online publication date: 22-Apr-2024
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    cover image ACM Conferences
    WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
    February 2023
    1345 pages
    ISBN:9781450394079
    DOI:10.1145/3539597
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 27 February 2023

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    Author Tags

    1. causal interpretation
    2. recommender system
    3. repeat intention
    4. scenario learning

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    View all
    • (2024)Multimodal-aware Multi-intention Learning for RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681412(5663-5672)Online publication date: 28-Oct-2024
    • (2024)MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender SystemsProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635859(434-442)Online publication date: 4-Mar-2024
    • (2024)MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion ClassificationInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00489-617:1Online publication date: 22-Apr-2024
    • (2023)BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608781(625-636)Online publication date: 14-Sep-2023

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