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Multi-Domain Sequential Recommendation via Domain Space Learning

Published: 11 July 2024 Publication History

Abstract

This paper explores Multi-Domain Sequential Recommendation (MDSR), an advancement of Multi-Domain Recommendation that incorporates sequential context. Recent MDSR approach exploits domain-specific sequences, decoupled from mixed-domain histories, to model domain-specific sequential preference, and use mixeddomain histories to model domain-shared sequential preference. However, the approach faces challenges in accurately obtaining domain-specific sequential preferences in the target domain, especially when users only occasionally engage with it. In such cases, the history of users in the target domain is limited or not recent, leading the sequential recommender system to capture inaccurate domain-specific sequential preferences. To address this limitation, this paper introduces Multi-Domain Sequential Recommendation via Domain Space Learning (MDSR-DSL). Our approach utilizes cross-domain items to supplement missing sequential context in domain-specific sequences. It involves creating a "domain space" to maintain and utilize the unique characteristics of each domain and a domain-to-domain adaptation mechanism to transform item representations across domain spaces. To validate the effectiveness of MDSR-DSL, this paper extensively compares it with state-of-the-art MD(S)R methods and provides detailed analyses.

References

[1]
Nawaf Alharbi and Doina Caragea. 2022. Cross-Domain Attentive Sequential Recommendations Based on General and Current User Preferences (CD-ASR). In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21). Association for Computing Machinery, New York, NY, USA, 48--55.
[2]
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
[3]
Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, and Bin Wang. 2022a. Contrastive Cross-Domain Sequential Recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22). Association for Computing Machinery, New York, NY, USA, 138--147.
[4]
Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, and Bin Wang. 2023. Towards Universal Cross-Domain Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM). 78--86.
[5]
Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu, and Bin Wang. 2022b. DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 267--277.
[6]
Liu Chong, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li, Lijun Wu, Min Zhang, and Leyu Lin. 2023. CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3901--3913.
[7]
Qiang Cui, Tao Wei, Yafeng Zhang, and Qing Zhang. 2020. HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation. In ORSUM@ RecSys.
[8]
Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Zhi-Hua Zhou (Ed.). International Joint Conferences on Artificial Intelligence Organization, 2483--2489.
[9]
Qiushan Guo, Xinjiang Wang, Yichao Wu, Zhipeng Yu, Ding Liang, Xiaolin Hu, and Ping Luo. 2020. Online knowledge distillation via collaborative learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11020--11029.
[10]
Li He, Xianzhi Wang, Dingxian Wang, Haoyuan Zou, Hongzhi Yin, and Guandong Xu. 2023. Simplifying Graph-Based Collaborative Filtering for Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM '23). Association for Computing Machinery, New York, NY, USA, 60--68.
[11]
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 (WWW'16). 507--517.
[12]
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the Knowledge in a Neural Network. arxiv: 1503.02531
[13]
Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 667--676.
[14]
Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, and Hwanjo Yu. 2024. Multi-Domain Recommendation to Attract Users via Domain Preference Modeling. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 8582--8590.
[15]
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, and Hwanjo Yu. 2020. DE-RRD: A knowledge distillation framework for recommender system. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 605--614.
[16]
SeongKu Kang, Junyoung Hwang, Wonbin Kweon, and Hwanjo Yu. 2021. Topology Distillation for Recommender System. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD '21). Association for Computing Machinery, New York, NY, USA, 829--839. https://doi.org/10.1145/3447548.3467319
[17]
SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1563--1572.
[18]
W. Kang and J. McAuley. 2018. Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Los Alamitos, CA, USA, 197--206.
[19]
Diederik Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR). San Diega, CA, USA.
[20]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[21]
Wonbin Kweon, SeongKu Kang, and Hwanjo Yu. 2021. Bidirectional distillation for top-K recommender system. In Proceedings of the Web Conference 2021. 3861--3871.
[22]
Jae-woong Lee, Minjin Choi, Jongwuk Lee, and Hyunjung Shim. 2019. Collaborative distillation for top-N recommendation. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 369--378.
[23]
Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, Xiaohu Qie, and Di Niu. 2023 a. One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. ACM.
[24]
Huiyuan Li, Li Yu, Xi Niu, Youfang Leng, and Qihan Du. 2023 b. Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate. ACM Trans. Knowl. Discov. Data, Vol. 18, 1, Article 8 (aug 2023), bibinfonumpages28 pages.
[25]
Pan Li and Alexander Tuzhilin. 2020. Ddtcdr: Deep dual transfer cross domain recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining. 331--339.
[26]
Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. 2020. Cross domain recommendation via bi-directional transfer graph collaborative filtering networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 885--894.
[27]
Zhiwei Liu, Yongjun Chen, Jia Li, Philip S Yu, Julian McAuley, and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021).
[28]
Chen Ma, Peng Kang, and Xue Liu. 2019a. Hierarchical Gating Networks for Sequential Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 825--833.
[29]
Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Peiyu Liu, Jun Ma, and Maarten de Rijke. 2022. Mixed Information Flow for Cross-Domain Sequential Recommendations. ACM Trans. Knowl. Discov. Data, Vol. 16, 4, Article 64 (jan 2022), bibinfonumpages32 pages. https://doi.org/10.1145/3487331
[30]
Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019b. ?-Net: A Parallel Information-sharing Network for Shared-account Cross-domain Sequential Recommendations (SIGIR'19). New York, NY, USA, 685--694.
[31]
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In IJCAI, Vol. 17. 2464--2470.
[32]
Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, and Bo Tang. 2023. Multi-domain Recommendation with Embedding Disentangling and Domain Alignment. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23). Association for Computing Machinery, New York, NY, USA, 1917--1927.
[33]
Chung Park, Taesan Kim, Taekyoon Choi, Junui Hong, Yelim Yu, Mincheol Cho, Kyunam Lee, Sungil Ryu, Hyungjun Yoon, Minsung Choi, and Jaegul Choo. 2023. Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM '23). Association for Computing Machinery, New York, NY, USA, 2024--2033.
[34]
Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, and Victor Sheng. 2023. Meta-optimized contrastive learning for sequential recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 89--98.
[35]
Pengjie Ren, Yujie Lin, Muyang Ma, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations. CoRR, Vol. abs/1910.02448 (2019). showeprint[arXiv]1910.02448 http://arxiv.org/abs/1910.02448
[36]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI '09). AUAI Press, Arlington, Virginia, USA, 452--461.
[37]
Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 650--658.
[38]
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, Vol. 15, 56 (2014), 1929--1958. http://jmlr.org/papers/v15/srivastava14a.html
[39]
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.
[40]
Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei, and Fei Fang. 2023. Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation. arXiv preprint arXiv:2302.14438 (2023).
[41]
Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).
[42]
Chenyang Wang, Zhefan Wang, Yankai Liu, Yang Ge, Weizhi Ma, Min Zhang, Yiqun Liu, Junlan Feng, Chao Deng, and Shaoping Ma. 2022. Target interest distillation for multi-interest recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2007--2016.
[43]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 165--174. https://doi.org/10.1145/3331184.3331267
[44]
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE). IEEE, 1259--1273.
[45]
Xiaoxin Ye, Yun Li, and Lina Yao. 2023. DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys '23). Association for Computing Machinery, New York, NY, USA, 479--490. https://doi.org/10.1145/3604915.3608780
[46]
Yu Zhang, Bin Cao, and Dit-Yan Yeung. 2010. Multi-Domain Collaborative Filtering. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (Catalina Island, CA) (UAI'10). AUAI Press, Arlington, Virginia, USA, 725--732.
[47]
Ying Zhang, Tao Xiang, Timothy M. Hospedales, and Huchuan Lu. 2018. Deep Mutual Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48]
Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang. 2022. Decoupled knowledge distillation. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. 11953--11962.
[49]
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen. 2022a. Filter-enhanced MLP is All You Need for Sequential Recommendation. In Proceedings of WWW'22. Association for Computing Machinery, New York, NY, USA, 2388--2399.
[50]
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen. 2022b. Filter-enhanced MLP is All You Need for Sequential Recommendation. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW '22). Association for Computing Machinery, New York, NY, USA, 2388--2399.
[51]
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation. In IJCAI. 3001--3008.
[52]
Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, and Guanfeng Liu. 2023. A Unified Framework for Cross-Domain and Cross-System Recommendations. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 2 (2023), 1171--1184.
[53]
Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1507--1515.

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    cover image ACM Conferences
    SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2024
    3164 pages
    ISBN:9798400704314
    DOI:10.1145/3626772
    This work is licensed under a Creative Commons Attribution International 4.0 License.

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    Published: 11 July 2024

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    1. cross-domain recommendation
    2. sequential recommendation

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