iAgent: LLM Agent as a Shield between User and Recommender Systems Overlooks the Literature on User-Side Recommender Systems
iAgent [Xu et al., ACL 2025] proposes placing an agent between a provider-side recommender system and the user. The agent searches for relevant knowledge and reranks the recommendations produced by the platform, with the goal of obtaining rankings that better reflect the user’s preferences. The paper motivates this approach as follows: Traditional recommender systems usually […]
