Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach
This work addresses the challenge of accurately detecting user struggle for web search engines, representing an incremental improvement by integrating psychological insights into existing methods.
The paper tackled the problem of detecting user struggle in web search by challenging the assumption that effort directly correlates with frustration, proposing a feature modulation method based on psychological reversal theory. The method statistically significantly improved state-of-the-art struggle detection methods, as confirmed by evaluations on week-long web search logs.
Searcher struggle is important feedback to Web search engines. Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments. Their underlying assumption is that the more effort a user spends, the more struggling the user may be. However, recent studies have suggested this simple association might be incorrect. This paper proposes a new feature modulation method for struggle detection and refers to the reversal theory in psychology. The reversal theory (RT) points out that instead of having a static personality trait, people constantly switch between opposite psychological states, complicating the relationship between the efforts they spend and the level of frustration they feel. Supported by the theory, our method modulates the effort-based features based on RT's bi-modal arousal model. Evaluations on week-long Web search logs confirm that the proposed method can statistically significantly improve state-of-the-art struggle detection methods.