RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduce this setting to reranking small shortlisted candidate sets or judge reports mainly by semantic plausibility. We introduce Recommendation Atlas (Agentic Tool-Level Assessment for Shopping), or RecoAtlas, a benchmark and toolkit for evaluating shopping agents with behavior-grounded metrics. RecoAtlas complements held-out interaction metrics with learned utility proxies for relevance, complementarity, and diversity derived from interaction data, while separately measuring semantic coherence and explanation qualit...
| Authors | Imad Aouali, Flavian Vasile, Otmane Sakhi et al. |
|---|---|
| Primary category | cs.IR |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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