NTCIR

Biases—both human and algorithmic—pose critical challenges to the fairness and effectiveness of search and recommendation systems. Our research investigates how these biases are interwoven into users’ search interactions and system outputs, impacting relevance judgments, search behavior and satisfaction, and credibility assessments. Drawing from recent studies, I will explore how cognitive biases, such as decoy effect, reference dependence, and expectation confirmation biases, shape users’ interactions with and evaluations on Web search engines and recommender systems. We also examine the threshold priming effects in large language models (LLMs), revealing how these systems inherit and amplify human-like biases in information retrieval tasks. Beyond diagnosis, I introduce bias mitigation strategies at varying levels, including biased result obfuscation, adaptive ranking, and LLM-enhanced data augmentation, aimed at fostering fairer retrieval and recommendation outcomes. The talk and reported projects offer new insights into bias-aware user modeling and evaluation, and highlight the urgency of building responsible bias-resilient search and recommendation environments in the era of AI.

Dr. Jiqun Liu is an Assistant Professor of Data Science and an Affiliated Assistant Professor of Psychology at the University of Oklahoma. His research focuses on information retrieval, recommender systems, and human-AI interaction, with a particular emphasis on cognitive biases, bias-aware user modeling, and scalable bias mitigation strategies. Dr. Liu’s work bridges behavioral science and IR, and investigates how boundedly rational users interact with search engines, recommender systems, and large language models in information tasks. His research on bias-aware user modeling and IR evaluation received grant support from National Science Foundation (NSF) and Microsoft, and has been published at premier venues, such as ACM SIGIR, CHIIR, JASIST, IP&M, EMNLP, and TheWebConf. His work has also been introduced in a research monograph entitled A Behavioral Economics Approach to Interactive Information Retrieval: Understanding and Supporting Boundedly Rational Users by Springer Nature and presented through invited talks and tutorials to both academic audiences and tech industry practitioners.