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Found 5 papers, 1 papers with code
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect.
Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Controversial Topics
When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues.
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study
Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks.
Decoy Effect in Search Interaction: A Pilot Study
In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive information retrieval.
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques.
