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.

翻译：随着大语言模型（LLM）的发展，利用其丰富的语义理解能力来增强工业推荐系统（RecSys）的兴趣日益增长。传统推荐系统在通用搜索单元（GSU）和精确搜索单元（ESU）范式中依赖基于ID的嵌入进行用户序列建模，这存在信息密度低、知识孤立和泛化能力弱的问题。虽然LLM凭借其稠密语义表示和强大的泛化能力提供了互补优势，但直接将LLM嵌入应用于推荐系统面临关键挑战：表示与业务目标不匹配，以及表示无法与下游任务进行端到端的联合学习。本文提出QARM V2，这是一个统一的框架，旨在将LLM的语义理解与推荐系统的业务需求相桥接，用于用户序列建模。