BehaviorBench is a benchmark for foundation models on behavioral tasks that reveals fine-tuned behavioral models outperform general models on distributional alignment while general models lead on individual-level accuracy.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.
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BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks
BehaviorBench is a benchmark for foundation models on behavioral tasks that reveals fine-tuned behavioral models outperform general models on distributional alignment while general models lead on individual-level accuracy.
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IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
User-specific behavioral signals, especially prior search queries, outperform population-level demand patterns and static profiles for inferring gender, age, category, and size from underspecified e-commerce queries.