Discovering Common Motifs In Cursor Movement Data For Improving Web Search

Web search behavior and interaction data, such as mouse cursor movements, can provide valuable information on how searchers examine and engage with the web search results. This interaction data is far richer than traditional search click data, and can be used to improve search ranking, evaluation, and presentation. Unfortunately, the diversity and complexity inherent in this interaction data make it more difficult to capture salient behavior characteristics through traditional feature engineering. To address this problem, we introduce a novel approach of automatically discovering frequent subsequences, or motifs, in mouse cursor movement data. In order to scale our approach to realistic datasets, we introduce novel optimizations for motif discovery, specifically designed for mining cursor movement data. As a practical application, we show that by encoding the motifs discovered from thousands of real web search sessions as features, enables significant improvements on result relevance estimation and re-ranking tasks, compared to a state-of-the-art baseline that relies on extensive feature engineering. These results, complemented with visualization and qualitative analysis, demonstrate that our approach is able to automatically capture key characteristics of mouse cursor movement behavior, providing a valuable new tool for search behavior analysis.