Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search

TL;DR

This paper investigates the neural basis of non-click search behaviors, revealing brain signals associated with perceived usefulness of results, and demonstrates that brain data can improve usefulness estimation in zero-click search scenarios.

Contribution

It introduces a neuroimaging-based approach to understand non-click behaviors and develops models that leverage brain signals to estimate result usefulness beyond traditional content analysis.

Findings

Brain signals differ when examining useful vs. non-useful results.

EEG spectrum analysis indicates cognitive processes involved in relevance judgment.

Brain data can enhance usefulness prediction models in zero-click search.

Abstract

Web search heavily relies on click-through behavior as an essential feedback signal for performance improvement and evaluation. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click (especially non-click after examination) is regarded as a signal of irrelevance or uselessness. However, there are many cases where users do not click on any search results but still satisfy their information need with the contents of the results shown on the Search Engine Result Page (SERP). This raises the problem of measuring result usefulness and modeling user satisfaction in "Zero-click" search scenarios. Previous works have solved this issue by (1) detecting user satisfaction for abandoned SERP with context information and (2) considering result-level click necessity with external assessors' annotations. However, few works have investigated the reason behind non-click behavior and estimated the usefulness of non-click results. A challenge for this research question is how to collect valuable feedback for non-click results. With neuroimaging technologies, we design a lab-based user study and reveal differences in brain signals while examining non-click search results with different usefulness levels. The findings in significant brain regions and electroencephalogram~(EEG) spectrum also suggest that the process of usefulness judgment might involve similar cognitive functions of relevance perception and satisfaction decoding. Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i.e., content and context factors). Results show that it is feasible to utilize brain signals to improve usefulness estimation performance and enhancing human-computer interactions in "Zero-click" search scenarios.