We investigate how people interact with Web search engine result pages using eye-tracking, to provide a detailed understanding of the patterns of user attention. Previous research has examined the visual attention devoted to the 10 organic search results, and we extend this by also examining how gaze is distributed across other components of contemporary search engines, such as ads and related searches.This provides insights about searcher's interactions with the -whole page‖, and not just individual components. In addition, we used clustering techniques to identify groups of individuals, with distinct gaze patterns. The groups varied in how exhaustively they examined the search results and in what regions of the search result page they paid most attention to (organic results vs. ads). These results further our understanding of how attention is distributed across increasingly complex search result pages, and how individuals exhibit distinct patterns of attention and interaction.

We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document parts the user read in some way. We use this information on the subdocument level as implicit feedback for query expansion and reranking.We evaluated three different variants incorporating gaze data on the subdocument level and compared them against a baseline based on context on the document level. Our results show that considering reading behavior as feedback yields powerful improvements of the search result accuracy of ca. 32% in the general case. However, the extent of the improvements varies depending on the internal structure of the viewed documents and the type of the current information need.

Understanding the impact of individual and task differences on search result page examination strategies is important in developing improved search engines. Characterizing these effects using query and click data alone is common but insufficient since they provide an incomplete picture of result examination behavior. Cursor-or gaze-tracking studies reveal richer interaction patterns but are often done in small-scale laboratory settings. In this paper we leverage large-scale rich behavioral log data in a naturalistic setting. We examine queries, clicks, cursor movements, scrolling, and text highlighting for millions of queries on the Bing commercial search engine to better understand the impact of user, task, and user-task interactions on user behavior on search result pages (SERPs). By clustering users based on cursor features, we identify individual, task, and user-task differences in how users examine results which are similar to those observed in small-scale studies. Our findings have implications for developing search support for behaviorally-similar searcher cohorts, modeling search behavior, and designing search systems that leverage implicit feedback.

No abstract