Decoy Effect in Search Interaction: Understanding User Behavior and Measuring System Vulnerability
Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, Xiao-Ming Wu · ACM Transactions on Information Systems · 2024
This study addresses (1) the influence of the decoy effect, a cognitive bias where the presence of an inferior item alters preferences between two options, on users’ search interactions and (2) the measurement of information retrieval systems’ vulnerability to the decoy effect. 1 From the perspective of user behavior, this study investigates the influence of the decoy effect in information retrieval (IR) by examining how decoy results affect users’ interaction on search engine result pages (SERPs), particularly in terms of click-through likelihood, browsing dwell time, and perceived document usefulness. We conducted an experiment based upon regression analysis on user interaction logs from three user study datasets which in total encompass 24 topics, 841 unique search sessions, and 2,685 queries. The findings indicate that decoys significantly increase the likelihood of document clicks and perceived usefulness. To investigate whether the influence of the decoy varies across different levels of task difficulty and user knowledge, we ran an additional experiment on one of the three datasets, which encompasses 6 topics, 166 search sessions and 652 queries. The results indicate that when the task is less challenging, users are more likely to click on a document with a decoy. Additionally, they spend more time on the target document and assign it a higher usefulness score. Furthermore, users with lower knowledge levels about the topic tend to give higher usefulness ratings to the target document. Regarding IR system evaluation, this study provides empirical insights into measuring the vulnerability of text retrieval models to potential decoy effect. An evaluation metric, namely DEcoy Judgement and Assessment VUlnerability (DEJA-VU), is proposed to evaluate the possibility of a retrieval model ranking results in a way that could trigger decoy biases. The experiments on the Text REtrieval Conference (TREC) 19 Deep Learning (DL) passage retrieval task and the TREC 20 DL passage retrieval task demonstrate that ColBERT and SPLADE show higher relevance-oriented retrieval effectiveness while also displaying lower vulnerability to decoy effect. Overall, this work advances the understanding of decoy effect, a well-established concept in cognitive psychology and behavioral economics, in a novel application field (i.e., Information Retrieval). It contributes to modeling users’ search behavior in the context of cognitive biases, as well as assessment of the vulnerability of systems and ranking algorithms to the decoy effect.