RecoAtlas is a benchmark that evaluates LLM recommendation agents on behavior-grounded metrics for relevance, complementarity, and diversity in addition to semantic coherence.
arXiv:2103.08057 [cs.LG] https://arxiv.org/abs/2103.08057
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Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
Data portability scenarios in algorithmic pluralism produce varying effects on user utility across different recommendation algorithms.
citing papers explorer
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RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
RecoAtlas is a benchmark that evaluates LLM recommendation agents on behavior-grounded metrics for relevance, complementarity, and diversity in addition to semantic coherence.
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Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
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Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem
Data portability scenarios in algorithmic pluralism produce varying effects on user utility across different recommendation algorithms.