AI 中文总结
本文提出 TSPORec 方法，通过三阶段流程与新型代理奖励选择信息 token，在提升 LLM 序列推荐性能的同时降低计算成本，实验显示其较基线方法性能提升最高 31.25%、效率提升最高 63.4%。
AI 中文摘要
大型语言模型（LLMs）已成为改进推荐系统的强大工具，其有效性源于利用丰富文本信息及基于用户交互历史建模异构用户偏好的能力。但受大规模深度架构影响，基于 LLM 的序列推荐方法通常推理成本高，投资回报率低。为缓解成本问题，现有诸多方法仅使用物品描述的前几个 token，却无意中丢弃了全文本中的有价值信息，导致推荐性能欠佳。为解决此局限，本文提出一种用于 LLM 序列推荐的偏好优化 token 选择新方法，即 TSPORec，可精准定位全文本中的信息 token 以提升推荐性能。具体而言，本文设计了一个三阶段流程来选择信息 token，并引入一种新型代理奖励以助力实现。TSPORec 不仅提升了推荐性能，还提高了计算效率。在两个模型及数据集上开展的大量实验表明，与六种基线方法相比，本文方法的性能提升最高达 31.25%，效率提升最高达 63.4%。代码可在此 https URL 获取。
英文摘要
Large Language Models (LLMs) have emerged as powerful tools for improving recommendation systems. The effectiveness of LLMs arises from their ability to harness rich textual information and their capacity to model heterogeneous user preferences based on users' interaction history. However, due to the large-scale and deep architectures, LLM-based sequential recommendation approaches generally incur high inference costs, resulting in a low return on investment. To mitigate this cost, many existing approaches resort to using only the first few tokens of item descriptions, which inadvertently discards valuable information contained in the full text, thereby leading to suboptimal recommendation performance. To address this limitation, we propose a novel Token Selection approach for Preference Optimization in LLM-based sequential Recommendation, i.e., TSPORec, which accurately pinpoints informative tokens throughout the entire textual content to improve recommendation performance. Specifically, we design a three-stage pipeline to select informative tokens and introduce a novel proxy reward to facilitate the implementation. TSPORec not only enhances recommendation performance but also improves computational efficiency. Extensive experiments across two models and datasets demonstrate the superb performance (up to 31.25%) and efficiency (up to 63.4%) of our approach compared with six baseline approaches. Code is available at https://github.com/WNQzhu/TSPORec.git.
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