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.

Passage retrieval is a crucial first step of automatic Question Answering (QA).While existing passage retrieval algorithms are effective at selecting document passages most similar to the question, or those that contain the expected answer types, they do not take into account which parts of the document the searchers actually found useful.We propose, to the best of our knowledge, the first successful attempt to incorporate searcher examination data into passage retrieval for question answering.Specifically, we exploit detailed examination data, such as mouse cursor movements and scrolling, to infer the parts of the document the searcher found interesting, and then incorporate this signal into passage retrieval for QA.Our extensive experiments and analysis demonstrate that our method significantly improves passage retrieval, compared to using textual features alone.As an additional contribution, we make available to the research community the code and the search behavior data used in this study, with the hope of encouraging further research in this area.

Query-biased search result summaries, or "snippets", help users decide whether a result is relevant for their information need, and have become increasingly important for helping searchers with difficult or ambiguous search tasks. Previously published snippet generation algorithms have been primarily based on selecting document fragments most similar to the query, which does not take into account which parts of the document the searchers actually found useful. We present a new approach to improving result summaries by incorporating post-click searcher behavior data, such as mouse cursor movements and scrolling over the result documents. To achieve this aim, we develop a method for collecting behavioral data with precise association between searcher intent, document examination behavior, and the corresponding document fragments. In turn, this allows us to incorporate page examination behavior signals into a novel Behavior-Biased Snippet generation system (BeBS). By mining searcher examination data, BeBS infers document fragments of most interest to users, and combines this evidence with text-based features to select the most promising fragments for inclusion in the result summary. Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We believe that this work opens a new direction for improving search result presentation, and we make available the code and the search behavior data used in this study to encourage further research in this area.

A better understanding of strategies and behavior of successful searchers is crucial for improving the experience of all searchers. However, research of search behavior has been struggling with the tension between the relatively small-scale, but controlled lab studies, and the large-scale log-based studies where the searcher intent and many other important factors have to be inferred. We present our solution for performing controlled, yet realistic, scalable, and reproducible studies of searcher behavior. We focus on difficult informational tasks, which tend to frustrate many users of the current web search technology. First, we propose a principled formalization of different types of "success" for informational search, which encapsulate and sharpen previously proposed models. Second, we present a scalable game-like infrastructure for crowdsourcing search behavior studies, specifically targeted towards capturing and evaluating successful search strategies on informational tasks with known intent. Third, we report our analysis of search success using these data, which confirm and extends previous findings. Finally, we demonstrate that our model can predict search success more effectively than the existing state-of-the-art methods, on both our data and on a different set of log data collected from regular search engine sessions. Together, our search success models, the data collection infrastructure, and the associated behavior analysis techniques, significantly advance the study of success in web search.