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Full Description
This book constitutes the conference proceedings of 6th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2025 and 2nd International Workshop on Information Retrieval for Understudied Users, IR4U2, held in Padua, Italy, on July 17, 2025.
For BIAS 2025, 3 full papers were carefully reviewed and selected from 7 submissions. They focus on data preparation, countermeasure development, evaluation design, and case studies on algorithmic bias in search and recommendation.
For IR4U2 2025, all 6 submitted full papers were carefully reviewed and accepted. They focus on user-centered artificial intelligence approaches aimed to better design, develop, and evaluate information retrieval systems that ensure true accessibility and inclusivity.
Contents
.- Bias and Fairness (Bias 2025).
.- Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking.
.- FAIR-MASK: Mitigating Bias in Dense Embedding Retrieval through Dimension Reduction.
.- Mitigating Algorithmic Bias through Sampling: The Role of Group Size and Sample Selection.
.- Understudied Users.
.- Balancing Sensory Needs, Interests and Personality: An Integrated Approach to Event Recommendations for Adults with Autism Spectrum Disorder.
.- Blurred Lines: Understanding the Fit of Song Lyrics in Music Catalogs That Can Reach Children Through Recommendations.
.- Can You Feel It? Exploring the Emotional Profile of LLM Responses to Children's Queries.
.- Finding Nemo: A Serious Game to Raise Children's Awareness of Information Pollution.
.- Sign Language-Based Conversational Product Search.
.- Towards Accessible Information Retrieval for Children with a Mild Intellectual Disability.
