用大模型语义理解提升推荐系统用户序列建模效果
QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling
- 构建统一框架，融合大模型语义与推荐业务目标
- 解决大模型嵌入与推荐任务不匹配的核心难题
- 适合关注推荐系统与大模型结合的研究者
随着大语言模型（LLMs）的发展，利用其丰富的语义理解能力提升工业级推荐系统（RecSys）成为研究热点。传统推荐系统在通用搜索单元（GSU）和精确搜索单元（ESU）范式下依赖基于ID的嵌入进行用户序列建模，存在信息密度低、知识孤立、泛化能力弱等问题。尽管大模型具备密集语义表示和强泛化能力，但直接将其嵌入应用于推荐系统面临两大挑战：表示与业务目标不匹配，以及无法端到端与下游任务联合学习。本文提出QARM V2，一个统一框架，将大模型的语义理解能力与推荐系统的业务需求相衔接，实现用户序列建模的高效融合。
原文摘要 · Abstract (English)
With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys). Traditional RecSys relies on ID-based embeddings for user sequence modeling in the General Search Unit (GSU) and Exact Search Unit (ESU) paradigm, which suffers from low information density, knowledge isolation, and weak generalization ability. While LLMs offer complementary strengths with dense semantic representations and strong generalization, directly applying LLM embeddings to RecSys faces critical challenges: representation unmatch with business objectives and representation unlearning end-to-end with downstream tasks. In this paper, we present QARM V2, a unified framework that bridges LLM semantic understanding with RecSys business requirements for user sequence modeling.
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