IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation

Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao

科研成果: 书/报告/会议事项章节会议稿件同行评审

13 引用 (Scopus)

摘要

Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) short-term: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) long-term: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an Interest Dynamics modeling framework using generative Neural Processes, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms the state-of-the-art on various evaluation metrics.

源语言英语
主期刊名WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining
出版商Association for Computing Machinery, Inc
481-489
页数9
ISBN(电子版)9781450394079
DOI
出版状态已出版 - 27 2月 2023
活动16th ACM International Conference on Web Search and Data Mining, WSDM 2023 - Singapore, 新加坡
期限: 27 2月 20233 3月 2023

出版系列

姓名WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining

会议

会议16th ACM International Conference on Web Search and Data Mining, WSDM 2023
国家/地区新加坡
Singapore
时期27/02/233/03/23

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