TUT4CRS: Time-aware User-preference Tracking for Conversational Recommendation System
Authors: 
Dongxiao He, Jinghan Zhang, Xiaobao Wang, Meng Ge, Zhiyong Feng, Longbiao Wang, Xiaoke MaAuthors Info & Claims
Pages 5856 - 5864
Abstract
The Conversational Recommendation System (CRS) aims to capture user dynamic preferences and provide item recommendations based on multi-turn conversations. However, effectively modeling these dynamic preferences faces challenges due to conversational limitations, which mainly manifests as limited turns in a conversation (quantity aspect) and low compliance with queries (quality aspect). Previous studies often address these challenges in isolation, overlooking their interconnected nature. The fundamental issue underlying both problems lies in the potential abrupt changes in user preferences, to which CRS may not respond promptly. We acknowledge that user preferences are influenced by temporal factors, serving as a bridge between conversation quantity and quality. Therefore, we propose a more comprehensive CRS framework called Time-aware User-preference Tracking for Conversational Recommendation System (TUT4CRS), leveraging time dynamics to tackle both issues simultaneously. Specifically, we construct a global time interaction graph to incorporate rich external information and establish a local time-aware weight graph based on this information to adeptly select queries and effectively model user dynamic preferences. Extensive experiments on two real-world datasets validate that TUT4CRS can significantly improve recommendation performance while reducing the number of conversation turns.
References
[1]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems, C.J. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger (Eds.), Vol. 26. Curran Associates, Inc.
[2]
Qian Chen, Zhiqiang Guo, Jianjun Li, and Guohui Li. 2023. Knowledge-enhanced multi-view graph neural networks for session-based recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 352--361.
[3]
Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016. Towards conversational recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 815--824.
[4]
Wei Chu and Seung-Taek Park. 2009. Personalized recommendation on dynamic content using predictive bilinear models. In Proceedings of the 18th international conference on World wide web. 691--700.
[5]
Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, and Wai Lam. 2021. Unified conversational recommendation policy learning via graph-based reinforcement learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1431--1441.
[6]
Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, and Li Deng. 2016. Towards end-to-end reinforcement learning of dialogue agents for information access. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 484--495.
[7]
Lu Fan, Jiashu Pu, Rongsheng Zhang, and Xiao-Ming Wu. 2023. Neighborhood-based hard negative mining for sequential recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2042--2046.
[8]
Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard De Melo, and Yongfeng Zhang. 2022. Path language modeling over knowledge graphsfor explainable recommendation. In Proceedings of the ACM Web Conference 2022. 946--955.
[9]
Chenhao Hu, Shuhua Huang, Yansen Zhang, and Yubao Liu. 2022. Learning to infer user implicit preference in conversational recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 256--266.
[10]
Di Jin, Meng Ge, Zhixuan Li, Wenhuan Lu, Dongxiao He, and Francoise Fogelman-Soulie. 2017. Using deep learning for community discovery in social networks. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 160--167.
[11]
Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, and Weixiong Zhang. 2018. Integrative network embedding via deep joint reconstruction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3407--3413.
[12]
Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, and Weixiong Zhang. 2022. Raw-gnn: Random walk aggregation based graph neural network. IJCAI (2022).
[13]
Di Jin, Xiaobao Wang, Dongxiao He, Jianwu Dang, and Weixiong Zhang. 2021. Robust Detection of Link Communities With Summary Description in Social Networks. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 6 (2021), 2737--2749. https://doi.org/10.1109/TKDE.2019.2958806
[14]
Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul CroProceedings of the 55th Annual Meeting of the Association for Computational Linguisticsok, Y-Lan Boureau, and Jason Weston. 2019. Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 1951--1961.
[15]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).
[16]
Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In Proceedings of the 13th International Conference on Web Search and Data Mining. 304--312.
[17]
Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, and Dawei Yin. 2018. Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1437--1447.
[18]
Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, and Tat-Seng Chua. 2020. Interactive path reasoning on graph for conversational recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2073--2083.
[19]
Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting Liu. 2020. Towards conversational recommendation over multi-type dialogs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1036--1049.
[20]
Tariq Mahmood and Francesco Ricci. 2009. Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM conference on Hypertext and hypermedia. 73--82.
[21]
Mingjie Qian, Yongsen Zheng, Jinghui Qin, and Liang Lin. 2023. HutCRS: Hierarchical User-Interest Tracking for Conversational Recommender System. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 10281--10290.
[22]
Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995--1000.
[23]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian Personalized Ranking from Implicit Feedback., 452--461 pages.arxiv: 1205.2618 [cs.IR]
[24]
Nikolai Sobotta. 2016. A Systematic Literature Review on the Relation of Information Technology and Information Overload. In 2016 49th Hawaii International Conference on System Sciences (HICSS). 858--867. https://doi.org/10.1109/HICSS.2016.111
[25]
Yueming Sun and Yi Zhang. 2018. Conversational recommender system. In The 41st international acm sigir conference on research & development in information retrieval. 235--244.
[26]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc.
[27]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017. Graph attention networks. stat, Vol. 1050, 20 (2017), 10--48550.
[28]
Xiaobao Wang, Yiqi Dong, Di Jin, Yawen Li, Longbiao Wang, and Jianwu Dang. 2023. Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 4 (Jun. 2023), 4702--4710. https://doi.org/10.1609/aaai.v37i4.25594
[29]
Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, and Xuelong Li. 2022. Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 534--540. https://doi.org/10.24963/ijcai.2022/76 Main Track.
[30]
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. 2016. Dueling network architectures for deep reinforcement learning. In International conference on machine learning. PMLR, 1995--2003.
[31]
Wei Wei, Sen Zhao, and Ding Zou. 2023. Recommendation System: A Survey and New Perspectives. World Scientific Annual Review of Artificial Intelligence, Vol. 1 (2023), 2330001.
[32]
Kerui Xu, Jingxuan Yang, Jun Xu, Sheng Gao, Jun Guo, and Ji-Rong Wen. 2021. Adapting user preference to online feedback in multi-round conversational recommendation. In Proceedings of the 14th ACM international conference on web search and data mining. 364--372.
[33]
Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, and Jian Pei. 2022. Multiple choice questions based multi-interest policy learning for conversational recommendation. In Proceedings of the ACM Web Conference 2022. 2153--2162.
[34]
Rongmei Zhao, Shenggen Ju, Jian Peng, Ning Yang, Fanli Yan, and Siyu Sun. 2022. Two-Level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2701--2710.
Index Terms
- TUT4CRS: Time-aware User-preference Tracking for Conversational Recommendation System
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October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
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- Dong Xu
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