Agent4RecRepository24/100 via “memory-augmented agent decision-making with contextual retrieval”
Recommender system simulator with 1,000 agents
Unique: Implements a memory system specifically designed for recommendation simulation where agents retrieve past interactions (watches, ratings, exits) to inform current decisions, integrating memory retrieval directly into the LLM prompt pipeline. Unlike generic RAG systems, the memory is structured around recommendation-specific actions (watch, rate, evaluate, exit) and is retrieved based on both temporal proximity and semantic relevance to the current recommendation context.
vs others: More sophisticated than stateless user simulators because agents maintain and reference interaction history, but requires careful memory management to avoid context window overflow and retrieval latency compared to simpler Markov-based user models.