Title: ULMRec: User-centric Large Language Model for Sequential Recommendation Show Chinese title

Title: ULMRec：面向用户的大规模语言模型用于序列推荐

Abstract: Recent advances in Large Language Models (LLMs) have demonstrated promising performance in sequential recommendation tasks, leveraging their superior language understanding capabilities. However, existing LLM-based recommendation approaches predominantly focus on modeling item-level co-occurrence patterns while failing to adequately capture user-level personalized preferences. This is problematic since even users who display similar behavioral patterns (e.g., clicking or purchasing similar items) may have fundamentally different underlying interests. To alleviate this problem, in this paper, we propose ULMRec, a framework that effectively integrates user personalized preferences into LLMs for sequential recommendation. Considering there has the semantic gap between item IDs and LLMs, we replace item IDs with their corresponding titles in user historical behaviors, enabling the model to capture the item semantics. For integrating the user personalized preference, we design two key components: (1) user indexing: a personalized user indexing mechanism that leverages vector quantization on user reviews and user IDs to generate meaningful and unique user representations, and (2) alignment tuning: an alignment-based tuning stage that employs comprehensive preference alignment tasks to enhance the model's capability in capturing personalized information. Through this design, ULMRec achieves deep integration of language semantics with user personalized preferences, facilitating effective adaptation to recommendation. Extensive experiments on two public datasets demonstrate that ULMRec significantly outperforms existing methods, validating the effectiveness of our approach. Show Chinese abstract

Abstract: 近年来，大型语言模型（LLMs）的进展在序列推荐任务中展示了有前景的性能，利用其卓越的语言理解能力。 然而，现有的基于LLM的推荐方法主要关注建模物品级别的共现模式，而未能充分捕捉用户级别的个性化偏好。 这是有问题的，因为即使用户表现出相似的行为模式（例如，点击或购买类似的物品），他们可能有根本不同的潜在兴趣。 为了解决这个问题，本文中我们提出 ULMRec，一个能够有效将用户个性化偏好整合到LLM中的框架，用于序列推荐。 考虑到物品ID和LLM之间存在语义差距，我们将用户历史行为中的物品ID替换为其对应的标题，使模型能够捕捉物品语义。 为了整合用户个性化偏好，我们设计了两个关键组件： (1) 用户索引：一种利用用户评论和用户ID上的向量量化生成有意义且唯一的用户表示的个性化用户索引机制，以及 (2) 对齐调优：一种基于对齐的调优阶段，采用全面的偏好对齐任务来增强模型捕捉个性化信息的能力。 通过这种设计，ULMRec实现了语言语义与用户个性化偏好的深度整合，促进了有效的推荐适应。 在两个公开数据集上的大量实验表明，ULMRec显著优于现有方法，验证了我们方法的有效性。