Georg Buscher

What do you see when you're surfing?: using eye tracking to predict salient regions of web pages

An understanding of how people allocate their visual attention when viewing Web pages is very important for Web authors, interface designers, advertisers and others. Such knowledge opens the door to a variety of innovations, ranging from improved Web page design to the creation of compact, yet recognizable, visual representations of long pages. We present an eye-tracking study in which 20 users viewed 361 Web pages while engaged in information foraging and page recognition tasks. From this data, we describe general location-based characteristics of visual attention for Web pages dependent on different tasks and demographics, and generate a model for predicting the visual attention that individual page elements may receive. Finally, we introduce the concept of fixation impact, a new method for mapping gaze data to visual scenes that is motivated by findings in vision research.

The good, the bad, and the random: an eye-tracking study of ad quality in web search

We investigate how people interact with Web search engine result pages using eye-tracking. While previous research has focused on the visual attention devoted to the 10 organic search results, this paper examines other components of contemporary search engines, such as ads and related searches. We systematically varied the type of task (informational or navigational), the quality of the ads (relevant or irrelevant to the query), and the sequence in which ads of different quality were presented. We measured the effects of these variables on the distribution of visual attention and on task performance. Our results show significant effects of each variable. The amount of visual attention that people devote to organic results depends on both task type and ad quality. The amount of visual attention that people devote to ads depends on their quality, but not the type of task. Interestingly, the sequence and predictability of ad quality is also an important factor in determining how much people attend to ads. When the quality of ads varied randomly from task to task, people paid little attention to the ads, even when they were good. These results further our understanding of how attention devoted to search results is influenced by other page elements, and how previous search experiences influence how people attend to the current page.

Eye movements as implicit relevance feedback

Reading detection is an important step in the process of automatic relevance feedback generation based on eye movements for information retrieval tasks. We describe a reading detection algorithm and present a preliminary study to find expressive eye movement measures.

Query expansion using gaze-based feedback on the subdocument level

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.

Large-scale analysis of individual and task differences in search result page examination strategies

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.

Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond

Reading is one of the most frequent activities of knowledge workers. Eye tracking can provide information on what document parts users read, and how they were read. This article aims at generating implicit relevance feedback from eye movements that can be used for information retrieval personalization and further applications. We report the findings from two studies which examine the relation between several eye movement measures and user-perceived relevance of read text passages. The results show that the measures are generally noisy, but after personalizing them we find clear relations between the measures and relevance. In addition, the second study demonstrates the effect of using reading behavior as implicit relevance feedback for personalizing search. The results indicate that gaze-based feedback is very useful and can greatly improve the quality of Web search. The article concludes with an outlook introducing attentive documents keeping track of how users consume them. Based on eye movement feedback, we describe a number of possible applications to make working with documents more effective.

Leaving so soon?: understanding and predicting web search abandonment rationales

Users of search engines often abandon their searches. Despite the high frequency of Web search abandonment and its importance to Web search engines, little is known about why searchers abandon beyond that it can be for good or bad reasons. In this paper, we ex-tend previous work by studying search abandonment using both a retrospective survey and an in-situ method that captures aban-donment rationales at abandonment time. We show that although satisfaction is a common motivator for abandonment, one-in-five abandonment instances does not relate to satisfaction. We also studied the automatic prediction of the underlying reason for ob-served abandonment. We used features of the query and the results, interaction with the result page (e.g., cursor movements, scrolling, clicks), and the full search session. We show that our classifiers can learn to accurately predict the reasons for observed search abandonment. Such accurate predictions help search providers estimate user satisfaction for queries without clicks, affording a more complete understanding of search engine performance.

Text 2.0

We discuss the idea of text responsive to reading and argue that the combination of eye tracking, text and real time interaction offers various possibilities to en- hance the reading experience. We present a number of prototypes and applications facilitating the users gaze in order to assist comprehension difficulties and show their benefit in a preliminary evaluation.

Segment-level display time as implicit feedback: a comparison to eye tracking

We examine two basic sources for implicit relevance feedback on the segment level for search personalization: eye tracking and display time. A controlled study has been conducted where 32 participants had to view documents in front of an eye tracker, query a search engine, and give explicit relevance ratings for the results. We examined the performance of the basic implicit feedback methods with respect to improved ranking and compared their performance to a pseudo relevance feedback baseline on the segment level and the original ranking of a Web search engine. Our results show that feedback based on display time on the segment level is much coarser than feedback from eye tracking. But surprisingly, for re-ranking and query expansion it did work as well as eye-tracking-based feedback. All behavior-based methods performed significantly better than our non-behavior-based baseline and especially improved poor initial rankings of the Web search engine. The study shows that segment-level display time yields comparable results as eye-tracking-based feedback. Thus, it should be considered in future personalization systems as an inexpensive but precise method for implicit feedback.

A robust realtime reading-skimming classifier

Distinguishing whether eye tracking data reflects reading or skimming already proved to be of high analytical value. But with a potentially more widespread usage of eye tracking systems at home, in the office or on the road the amount of environmental and experimental control tends to decrease. This in turn leads to an increase in eye tracking noise and inaccuracies which are difficult to address with current reading detection algorithms. In this paper we propose a method for constructing and training a classifier that is able to robustly distinguish reading from skimming patterns. It operates in real time, considering a window of saccades and computing features such as the average forward speed and angularity. The algorithm inherently deals with distorted eye tracking data and provides a robust, linear classification into the two classes read and skimmed. It facilitates reaction times of 750ms on average, is adjustable in its horizontal sensitivity and provides confidence values for its classification results; it is also straightforward to implement. Trained on a set of six users and evaluated on an independent test set of six different users it achieved a 86% classification accuracy and it outperformed two other methods.