AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

Abstract: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.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Databases (cs.DB); Digital Libraries (cs.DL)

arXiv:2609.31166 [cs.IR]

(or arXiv:2609.31166v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.31166 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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