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

• URL: http://arxiv.org/abs/2605.09338v1
• Date: Sun, 10 May 2026 05:24:28 GMT
• Title: A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
• Abstract summary: We propose a framework for MM-LLM-driven multimedia understanding.<n>Our methodology employs a tripartite architecture encompassing content interpretation, representation extraction, and systematic pipeline integration.<n> 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.
• Score: 13.743053021808604
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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