Mining touch interaction data on mobile devices to predict web search result relevance

Fine-grained search interactions in the desktop setting, such as mouse cursor movements and scrolling, have been shown valuable for understanding user intent, attention, and their preferences for Web search results. As web search on smart phones and tablets becomes increasingly popular, previously validated desktop interaction models have to be adapted for the available touch interactions such as pinching and swiping, and for the different device form factors. In this paper, we present, to our knowledge, the first in-depth study of modeling interactions on touch-enabled device for improving Web search ranking. In particular, we evaluate a variety of touch interactions on a smart phone as implicit relevance feedback, and compare them with the corresponding fine-grained interactions on a desktop computer with mouse and keyboard as the primary input devices. Our experiments are based on a dataset collected from two user studies with 56 users in total, using a specially instrumented version of a popular mobile browser to capture the interaction data. We report a detailed analysis of the similarities and differences of fine-grained search interactions between the desktop and the smart phone modalities, and identify novel patterns of touch interactions indicative of result relevance. Finally, we demonstrate significant improvements to search ranking quality by mining touch interaction data.