A benchmark called UserArena was introduced to evaluate interactive user simulation for long-horizon shopping agents, targeting the gap in faithful recommendation world models that capture both the shopping environment and users' behavioral responses across extended interactions. The benchmark addresses agentic recommendation, in which agents pursue shopper goals through sequences of context-dependent decisions. The work is aimed at developing and evaluating such policies, which the source says require world models reflecting interactive shopping environments and user behavior over long interactions.
Agentic recommendation pursues shopper goals through sequences of context-dependent decisions. Developing and evaluating such policies requires faithful recommendation world models that capture both the interactive shopping environment and users' behavioral responses over long interactions. Existing
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