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arXiv · 2504.06780

CHIME: A Compressive Framework for Holistic Interest Modeling

Yong Bai·Rui Xiang·Kaiyuan Li·Yongxiang Tang·Yanhua Cheng·Xialong Liu·Peng Jiang·Kun Gai
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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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Yong Bai, Rui Xiang, Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Xialong Liu, Peng Jiang, Kun Gai. 2025-04-09. CHIME: A Compressive Framework for Holistic Interest Modeling. https://arxiv.org/abs/2504.06780

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