Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling

• URL: http://arxiv.org/abs/2110.11337v4
• Date: Wed, 12 Jul 2023 08:48:42 GMT
• Title: Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
• Authors: Bei Yang, Jie Gu, Ke Liu, Xiaoxiao Xu, Renjun Xu, Qinghui Sun, Hong Liu
• Abstract summary: We propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. LURM consists of two cascaded sub-models: (I) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 105) SMEN achieves almost dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests.
• Score: 11.698166058448555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cognition tasks, is a promising direction being more valuable and economical than task-specific representation learning. With the rapid development of Internet service platforms, user behaviors have been accumulated continuously. However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. Specifically, LURM consists of two cascaded sub-models: (I) Bag-of-Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5); (II) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of BoI features to multiple low-dimensional user representations. Specially, SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user interests. Experiments on several benchmark datasets show that our approach outperforms state-of-the-art general-purpose representation methods.

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