User Interaction Sequences for Search Satisfaction Prediction

Detecting and understanding implicit measures of user satisfaction are essential for meaningful experimentation aimed at enhancing web search quality. While most existing studies on satisfaction prediction rely on users' click activity and query reformulation behavior, often such signals are not available for all search sessions and as a result, not useful in predicting satisfaction. On the other hand, user interaction data (such as mouse cursor movement) is far richer than just click data and can provide useful signals for predicting user satisfaction. In this work, we focus on considering holistic view of user interaction with the search engine result page (SERP) and construct detailed universal interaction sequences of their activity. We propose novel ways of leveraging the universal interaction sequences to automatically extract informative, interpretable subsequences. In addition to extracting frequent, discriminatory and interleaved subsequences, we propose a Hawkes process model to incorporate temporal aspects of user interaction. Through extensive experimentation we show that encoding the extracted subsequences as features enables us to achieve significant improvements in predicting user satisfaction. We additionally present an analysis of the correlation between various subsequences and user satisfaction. Finally, we demonstrate the usefulness of the proposed approach in covering abandonment cases. Our findings provide a valuable tool for fine-grained analysis of user interaction behavior for metric development.