语义收敛：通过两阶段对齐与行为语义分词协调推荐系统
Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization
AI总结：
本文提出一种融合传统推荐模型与LLMs的新颖框架，通过Alignment Tokenization模块和监督学习任务实现协同信号与语言语义的两阶段对齐，并采用预缓存top-K结果优化在线推理，显著提升了召回率和系统可扩展性。
摘要：
大型语言模型（LLMs）具备卓越的推理能力，擅长从历史行为中洞察用户的深层兴趣，从而为推荐系统的发展提供了极具前景的途径。然而，推荐系统中常见的稀疏协同语义与 LLMs 内部的密集 token 表示之间仍存在显著差异。在本研究中，我们提出了一个新颖的框架，将传统推荐模型与 LLMs 的强大能力和谐地融合在一起。我们通过所提出的 Alignment Tokenization 模块，将 ItemIDs 转换为在语义上与 LLMs 空间对齐的序列，以此启动这一整合过程。此外，我们设计了一系列专门的监督学习任务，旨在将协同信号与自然语言语义的细微差别进行对齐。为确保实际适用性，我们通过为每个用户预缓存 top-K 结果来优化在线推理，从而降低延迟并提高效率。大量实验证据表明，我们的模型显著提升了召回率指标，并展现出推荐系统卓越的可扩展性。
英文摘要：
Large language models (LLMs), endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation systems and the dense token representations within LLMs. In our study, we propose a novel framework that harmoniously merges traditional recommendation models with the prowess of LLMs. We initiate this integration by transforming ItemIDs into sequences that align semantically with the LLMs space, through the proposed Alignment Tokenization module. Additionally, we design a series of specialized supervised learning tasks aimed at aligning collaborative signals with the subtleties of natural language semantics. To ensure practical applicability, we optimize online inference by pre-caching the top-K results for each user, reducing latency and improving effciency. Extensive experimental evidence indicates that our model markedly improves recall metrics and displays remarkable scalability of recommendation systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
