22 citations · 27 across the 7 of their papers we have counts for
4 papers · 1 filter
User Preference Induction with LLMs for Offline Top-N Recommendation Evaluation
David Otero, Javier Parapar
Offline evaluation is the standard methodology for comparing top-N recommender systems, yet it relies on incomplete relevance information. In most benchmark datasets, only a small…
Learning Evidence of Depression Symptoms via Prompt Induction
Eliseo Bao, Anxo Perez, David Otero +1
Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., onl…
Hybrid Pooling with LLMs via Relevance Context Learning
David Otero, Javier Parapar
High-quality relevance judgements over large query sets are essential for evaluating Information Retrieval (IR) systems, yet manual annotation remains costly and time-consuming. La…
LLM-Assisted Pseudo-Relevance Feedback
David Otero, Javier Parapar
Query expansion is a long-standing technique to mitigate vocabulary mismatch in ad hoc Information Retrieval. Pseudo-relevance feedback methods, such as RM3, estimate an expanded q…