Abstract
Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS works mainly focus on the single conversation (subsession) that the user quits after a successful recommendation, neglecting the common scenario where the user has multiple conversations (multi-subsession) over a short period. Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where the user would still resort to CRS after several subsessions and might preserve vague interests, and the system would proactively ask attributes to activate user interests in the current subsession. To fill the gap in this new CRS scenario, we devise a novel framework called Multi-Subsession Conversational Recommender with Activation Attributes (MSCAA). Specifically, we first develop a context-aware recommendation module, comprehensively modeling user interests from historical interactions, previous subsessions, and feedback in the current subsession. Furthermore, an attribute selection policy module is proposed to learn a flexible strategy for asking appropriate attributes to elicit user interests. Finally, we design a conversation policy module to manage the above two modules to decide actions between asking and recommending. Extensive experiments on four datasets verify the effectiveness of our MSCAA framework for the proposed MSMCR setting (More details of our work are presented in https://arxiv.org/pdf/2310.13365v1.pdf).
Y. Ji and Q. Shen– Contributed equally to this research.
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Acknowledgments
The work is partially supported by the National Nature Science Foundation of China (No. 62376199, 62076184, 62076182) and Shanghai Science and Technology Plan Project (No.21DZ1204800).
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ji, Y. et al. (2024). Towards Multi-subsession Conversational Recommendation. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_15
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DOI: https://doi.org/10.1007/978-981-97-2262-4_15
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