MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling

• URL: http://arxiv.org/abs/2512.07216v1
• Date: Mon, 08 Dec 2025 06:55:13 GMT
• Title: MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
• Authors: Bin Wu, Feifan Yang, Zhangming Chan, Yu-Ran Gu, Jiawei Feng, Chao Yi, Xiang-Rong Sheng, Han Zhu, Jian Xu, Mang Ye, Bo Zheng,
• Abstract summary: We present a systematic analysis of how to leverage multimodal signals across both stages of lifelong modeling framework.<n>We propose MUSE, a simple yet effective multimodal search-based framework.<n>MUSE has been deployed in Taobao display advertising system, enabling 100K-length user behavior sequence modeling.
• Score: 48.18456242206804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization on long-tail items and limited semantic expressiveness. While recent work explores multimodal representations for behavior retrieval in the General Search Unit (GSU), they often neglect multimodal integration in the fine-grained modeling stage -- the Exact Search Unit (ESU). In this work, we present a systematic analysis of how to effectively leverage multimodal signals across both stages of the two-stage lifelong modeling framework. Our key insight is that simplicity suffices in the GSU: lightweight cosine similarity with high-quality multimodal embeddings outperforms complex retrieval mechanisms. In contrast, the ESU demands richer multimodal sequence modeling and effective ID-multimodal fusion to unlock its full potential. Guided by these principles, we propose MUSE, a simple yet effective multimodal search-based framework. MUSE has been deployed in Taobao display advertising system, enabling 100K-length user behavior sequence modeling and delivering significant gains in top-line metrics with negligible online latency overhead. To foster community research, we share industrial deployment practices and open-source the first large-scale dataset featuring ultra-long behavior sequences paired with high-quality multimodal embeddings. Our code and data is available at https://taobao-mm.github.io.

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