EmbSum：利用大语言模型的摘要能力实现基于内容的推荐
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
AI总结：
针对基于内容的推荐需求，本文提出EmbSum框架，结合预训练编解码模型、多头注意力层与LLM监督的用户兴趣摘要生成，实现离线预计算与长期兴趣建模，在两个跨领域数据集上以更少参数超越现有最优方法，还可输出兴趣摘要。
摘要：
在数字世界中，基于内容的推荐系统在为用户提供个性化内容方面发挥着关键作用。本文提出了一种名为EmbSum的新型框架，该框架支持对用户和候选项进行离线预计算，同时能够捕捉用户交互历史中的互动关系。通过利用预训练的编码器-解码器模型和多头注意力（poly-attention）层，EmbSum生成用户多头嵌入（User Poly-Embedding, UPE）和内容多头嵌入（Content Poly-Embedding, CPE），用于计算用户与候选项之间的相关性得分。EmbSum通过在大语言模型（LLM）的监督下生成用户兴趣摘要，主动学习用户的长期交互历史。研究在两个不同领域的数据集上验证了EmbSum的有效性，其以更少的参数超越了当前最优（SoTA）方法，取得了更高的准确率。此外，该模型生成用户兴趣摘要的能力作为一项有价值的副产品，进一步提升了其在个性化内容推荐中的实用性。
英文摘要：
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world. In this work, we introduce EmbSum, a novel framework that enables offline pre-computations of users and candidate items while capturing the interactions within the user engagement history. By utilizing the pretrained encoder-decoder model and poly-attention layers, EmbSum derives User Poly-Embedding (UPE) and Content Poly-Embedding (CPE) to calculate relevance scores between users and candidate items. EmbSum actively learns the long user engagement histories by generating user-interest summary with supervision from large language model (LLM). The effectiveness of EmbSum is validated on two datasets from different domains, surpassing state-of-the-art (SoTA) methods with higher accuracy and fewer parameters. Additionally, the model's ability to generate summaries of user interests serves as a valuable by-product, enhancing its usefulness for personalized content recommendations.
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。
