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Recommendation System Evaluation
SimUSER: When Language Models Pretend to Be Believable Users in Recommender Systems
Published:10/4/2024
Believable User Agent FrameworkUser Profile Construction from Historical DataRecommendation System EvaluationSelf-Consistent User Persona ModelingMemory Module and Knowledge Graph Memory
SimUSER is an agent framework that serves as believable and costeffective human proxies for evaluating recommender systems, leveraging foundation models to enrich user profiles and enhance the believability of user behavior across various recommendation domains.
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ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Published:6/27/2025
Multi-Agent Personalized Recommendation SystemAugmented Retrieval-Generation FrameworkLLM-based Recommendation SystemsDynamic User Preference ModelingRecommendation System Evaluation
The ARAG framework enhances personalized recommendations by integrating a multiagent collaboration into RetrievalAugmented Generation. It employs agents for user understanding, natural language inference, context summarization, and item ranking, outperforming traditional RAG me
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