通过压缩词表扩展提升大模型推荐系统性能
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems
- 将物品映射到扩展词表中，利用大模型序列理解能力
- 压缩嵌入层实现工业级大规模应用，性能显著提升
- 适合需要高效推荐的大规模平台使用
推荐系统在为用户提供相关内容方面发挥着关键作用。随着大语言模型（LLMs）的快速发展，研究人员开始利用LLMs构建更强大的推荐系统。然而，现有方法聚焦于对齐LLM与推荐任务，未能充分挖掘其序列信息处理能力，导致性能不佳。本文提出一种新框架——压缩词表扩展（CoVE）。在CoVE中，每个物品被分配唯一的扩展词表ID，有效利用大模型的序列理解能力，显著提升其在推荐任务中的表现。此外，通过压缩嵌入层，使CoVE适用于大规模工业场景。我们在多个推荐数据集上进行了全面实验，并与先前方法对比，验证了CoVE的有效性与高性能。代码已开源：https://github.com/HaochenZhang717/CoVE-official-Repo。
原文摘要 · Abstract (English)
Recommender systems play a pivotal role in providing relevant content to users. With the rapid development of large language models (LLMs), researchers have begun utilizing LLMs to build more powerful recommender systems. However, existing approaches that focus on aligning LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities, leading to suboptimal performance. In this paper, we propose a novel system called compressed vocabulary expansion (CoVE). In CoVE, each item is assigned a unique ID within the expanded vocabulary. Our framework effectively capitalizes on sequence understanding abilities of LLMs, significantly enhancing their performance on recommendation tasks. Additionally, we compress the embedding layer, making CoVE practical for large-scale industrial applications. The effectiveness and performance of CoVE are demonstrated through comprehensive experiments on multiple recommendation datasets and comparisons with prior works. Our code can be found at https://github.com/HaochenZhang717/CoVE-official-Repo.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。