AgentRecommender：LLM智能体使用户侧可定制推荐系统成为可能
AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side
浏览论文内容
中文总结 AI 辅助
针对平台推荐系统带来的点击诱饵等问题，提出AgentRecommender方法，利用LLM智能体的调查能力与内部知识，无需额外数据即可在用户侧灵活构建个性化推荐系统。
中文摘要 AI 辅助
推荐系统传统上是为平台开发的。然而，这引发了许多可能有利于平台锁定但对用户造成困扰的现象，例如点击诱饵、过滤气泡和假新闻的传播。最近，用户侧推荐系统被提出作为解决这一问题的新范式。如果用户部署自己的推荐系统，他们就不再受制于平台利益的支配。然而，构建用户侧推荐系统并非易事；特别是，为自己定制系统需要额外的数据。我们提出了AgentRecommender，一种利用LLM智能体的调查能力和内部知识来灵活构建用户侧推荐系统而无需额外数据的方法。AgentRecommender允许用户轻松创建符合自身偏好的推荐系统。
英文摘要
Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platform lock-in but are a nuisance to users, such as clickbait, filter bubbles, and the spread of fake news. Recently, user-side recommender systems have been proposed as a new paradigm for solving this problem. If users deploy their own recommender systems, they are no longer at the mercy of the platform's interests. However, building a user-side recommender system is not trivial; in particular, customizing one for oneself requires additional data. We propose AgentRecommender, a method that leverages the investigation capability and internal knowledge of LLM agents to flexibly build user-side recommender systems without additional data. AgentRecommender allows users to easily create recommender systems tailored to their own preferences.
发表机构
- National Institute of Informatics（国立信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。
