CHIME: A Compressive Framework for Holistic Interest Modeling

• URL: http://arxiv.org/abs/2504.06780v1
• Date: Wed, 09 Apr 2025 11:08:49 GMT
• Title: CHIME: A Compressive Framework for Holistic Interest Modeling
• Authors: Yong Bai, Rui Xiang, Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai,
• Abstract summary: We propose CHIME: A Compressive Framework for Holistic Interest Modeling.<n>It uses adapted large language models to encode complete user behaviors with heterogeneous inputs.<n>ChiME demonstrates superior ranking performance across diverse datasets.
• Score: 15.818669767036592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling holistic user interests is important for improving recommendation systems but is challenged by high computational cost and difficulty in handling diverse information with full behavior context. Existing search-based methods might lose critical signals during behavior selection. To overcome these limitations, we propose CHIME: A Compressive Framework for Holistic Interest Modeling. It uses adapted large language models to encode complete user behaviors with heterogeneous inputs. We introduce multi-granular contrastive learning objectives to capture both persistent and transient interest patterns and apply residual vector quantization to generate compact embeddings. CHIME demonstrates superior ranking performance across diverse datasets, establishing a robust solution for scalable holistic interest modeling in recommendation systems.

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