Eye-tracking study shows F-pattern and examination hypothesis from web search do not hold in carousel interfaces; users follow an L-pattern on clicks, ignore headings, and examination does not predict clicks as assumed.
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5 Pith papers cite this work. Polarity classification is still indexing.
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MIRA is a new benchmark for multi-category integrated retrieval built from real queries on a social science platform, with LLM assistance for topic descriptions and relevance labeling across four item categories.
Personalized soft prompts steer VLM attention to match user-specific gaze patterns, yielding better attention alignment and click prediction in recommendation simulations.
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
LLMs consistently overrate relevance of inadequate passages in IR evaluations due to biases toward length and lexical features rather than true content match.
citing papers explorer
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Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces
Eye-tracking study shows F-pattern and examination hypothesis from web search do not hold in carousel interfaces; users follow an L-pattern on clicks, ignore headings, and examination does not predict clicks as assumed.
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MIRA: An LLM-Assisted Benchmark for Multi-Category Integrated Retrieval
MIRA is a new benchmark for multi-category integrated retrieval built from real queries on a social science platform, with LLM assistance for topic descriptions and relevance labeling across four item categories.
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Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation
Personalized soft prompts steer VLM attention to match user-specific gaze patterns, yielding better attention alignment and click prediction in recommendation simulations.
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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
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When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
LLMs consistently overrate relevance of inadequate passages in IR evaluations due to biases toward length and lexical features rather than true content match.