Agent4RecRepository24/100 via “page-by-page recommendation interaction simulation with multi-action responses”
Recommender system simulator with 1,000 agents
Unique: Models recommendation interactions as multi-action sequences where agents see paginated results and decide which items to engage with and how (watch, rate, evaluate, exit), rather than single-item binary responses. The LLM generates actions conditioned on the agent's persona, memory, and the full page context, enabling realistic browsing behavior where users selectively engage with recommendations.
vs others: More realistic than single-action simulators (e.g., click/no-click) because it captures diverse user behaviors, but more computationally expensive due to multiple LLM calls per page and higher decision complexity.