Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
Not on the map. Why?
Abstract (arXiv)
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. It explores how decoy results alter users' interactions on search engine result pages, focusing on metrics like click-through likelihood, browsing time, and perceived document usefulness. By analyzing user interaction logs from multiple datasets, the study demonstrates that decoy results significantly affect users' behavior and perceptions. Furthermore, it investigates how different levels of task difficulty and user knowledge modify the decoy effect's impact, finding that easier tasks and lower knowledge levels lead to higher engagement with target documents. In terms of IR system evaluation, the study introduces the DEJA-VU metric to assess systems' susceptibility to the decoy effect, testing it on specific retrieval tasks. The results show differences in systems' effectiveness and vulnerability, contributing to our understanding of cognitive biases in search behavior and suggesting pathways for creating more balanced and bias-aware IR evaluations.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2024AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessmentcited by 20
- 2025Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Searchcited by 3
- 2024The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judgescited by 2
- 2025Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusingcited by 2
- 2026Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessmentcited by 1
- 2025RewardRank: Optimizing True Learning-to-Rank Utilitycited by 0
- 2026Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessment5 Jan 2026
- 2025Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing29 Nov 2025
- 2025RewardRank: Optimizing True Learning-to-Rank Utility19 Aug 2025
- 2025Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search2 Apr 2025
- 2024The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges23 Nov 2024
- 2024AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment24 Sep 2024