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arXiv · 2605.09338

A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems

Yiming Zhu·Xu Liu·Ziyun Xu·Zheng Wu·Joena Zhang·Sirius Chen·Chenheli Hua·Silvester Yao·Qichao Que·Wentao Shi·Junfeng Pan·Linhong Zhu
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Abstract

Conventional recommendation systems frequently fail to fully exploit the high-dimensional semantic signals inherent in multimedia content, thereby limiting the fidelity of user preference modeling. While Multimodal Large Language Models (MM-LLMs) offer robust mechanisms for interpreting such complex data, their integration into latency-constrained, industrial-scale architectures remains a significant challenge. To address this, we propose a generalized framework for MM-LLM-driven multimedia understanding. Our methodology employs a tripartite architecture encompassing content interpretation, representation extraction, and systematic pipeline integration, instantiated via a LLaMA2-based model that generates descriptive captions subsequently ingested as tokenized categorical features. Empirical evaluation demonstrates the efficacy of this approach, yielding a $0.35\%$ increase in offline AUC and a $0.02\%$ improvement in online metrics at scale, substantiating the practical viability of leveraging MM-LLMs to enhance large-scale recommendation performance.

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Yiming Zhu, Xu Liu, Ziyun Xu, Zheng Wu, Joena Zhang, Sirius Chen, Chenheli Hua, Silvester Yao, Qichao Que, Wentao Shi, Junfeng Pan, Linhong Zhu. 2026-05-10. A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems. https://doi.org/10.1145/3805712.3808431

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