Daniel J. Liebling

To personalize or not to personalize: modeling queries with variation in user intent

In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and peoples interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization.

Understanding the relationship between searchers' queries and information goals

We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.

Understanding and predicting personal navigation

This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Prediction is done via a straightforward form of personalization that takes advantage of the fact that people often use search engines to re-find previously viewed resources. In our approach, an individuals past navigational behavior is identified via query log analysis and used to forecast identical future navigational behavior by the same individual. We compare the potential value of personal navigation with general navigation identified using aggregate user behavior. Although consistent navigational behavior across users can be useful for identifying a subset of navigational queries, different people often use the same queries to navigate to different resources. This is true even for queries comprised of unambiguous company names or URLs and typically thought of as navigational. We build an understanding of what personal navigation looks like, and identify ways to improve its coverage and accuracy by taking advantage of peoples consistency over time and across groups of individuals.

Changing how people view changes on the web

The Web is a dynamic information environment. Web content changes regularly and people revisit Web pages frequently. But the tools used to access the Web, including browsers and search engines, do little to explicitly support these dynamics. In this paper we present DiffIE, a browser plug-in that makes content change explicit in a simple and lightweight manner. DiffIE caches the pages a person visits and highlights how those pages have changed when the person returns to them. We describe how we built a stable, reliable, and usable system, including how we created compact, privacy-preserving page representations to support fast difference detection. Via a longitudinal user study, we explore how DiffIE changed the way people dealt with changing content. We find that much of its benefit came not from exposing expected change, but rather from drawing attention to unexpected change and helping people build a richer understanding of the Web content they frequent.

Selfsourcing personal tasks

Large tasks can be overwhelming. For example, many people have thousands of digital photographs that languish in unorganized archives because it is difficult and time consuming to gather them into meaningful collections. Such tasks are hard to start because they seem to require long uninterrupted periods of effort to make meaningful progress. We propose the idea of selfsourcing as a way to help people to perform large personal information tasks by breaking them into manageable microtasks. Using ideas from crowdsourcing and task management, selfsourcing can help people take advantage of existing gaps in time and recover quickly from interruptions. We present several achievable selfsourcing scenarios and explore how they can facilitate information work in interruption-driven environments.

Gaze and mouse coordination in everyday work

Gaze tracking technology is increasingly common in desktop, laptop and mobile scenarios. Most previous research on eye gaze patterns during human-computer interaction has been confined to controlled laboratory studies. In this paper we present an in situ study of gaze and mouse coordination as participants went about their normal activities. We analyze the coordination between gaze and mouse, showing that gaze often leads the mouse, but not as much as previously reported, and in ways that depend on the type of target. Characterizing the relationship between the eyes and mouse in realistic multi-task settings highlights some new challenges we face in designing robust gaze-enhanced interaction techniques.

A longitudinal study of how highlighting web content change affects people's web interactions

The Web is constantly changing, but most tools used to access Web content deal only with what can be captured at a single instance in time. As a result, Web users may not have a good understanding of the changes that occur. In this paper we show that making Web content change explicitly visible allows people to interact with the Web in new ways. We present a longitudinal study in which 30 people used a Web browser plug-in that caches visited pages and highlights text changes to those pages when revisited. We used a survey to capture their understanding of Web page change and their own revisitation patterns at the beginning of use and after one month. For a majority of the participants, we also logged their Web page visits and associated content change. Exposing change is more valuable to our participants than initially expected, making them aware of how dynamic content they visit is and changing their interactions with it.

Anticipatory search: using context to initiate search

Identifying content for which a user may search has a variety of applications, including ranking and recommendation. In this poster, we examine how pre-search context can be used to predict content that the user will seek before they have even specified a search query. We call this anticipatory search. Using a log-based approach, we compare different methods for predicting the content to be searched using different attributes of the pre-query context and behavioral signals from previous visitors to the most recent browse URL. Each method covers different cases and shows promise for query-free anticipatory search on the Web.

Kinected browser: depth camera interaction for the web

Interest in and development of gesture interfaces has recently exploded, fueled in part by the release of Microsoft Corporations Kinect, a low-cost, consumer-packaged depth camera with integrated skeleton tracking. Depth-camera-based gestures can facilitate interaction with the Web on keyboard-and-mouse-free and/or multi-user technologies, such as large display walls or TV sets. We present a toolkit for bringing such gesture affordances into modern Web browsers using existing Web programming methods. Our framework is designed to enable Web programmers to incrementally add this capability with minimum effort by leveraging Web standard DOM structures and event models. We describe our frameworks design and architecture, and illustrate its usability and versatility.

Characterizing Email Search using Large-scale Behavioral Logs and Surveys

As the number of email users and messages continues to grow, search is becoming more important for finding information in personal archives. In spite of its importance, email search is much less studied than web search, particularly using large-scale behavioral log analysis. In this paper we report the results of a large-scale log analysis of email search and complement this with a survey to better understand email search intent and success. We characterize email search behaviors and highlight differences from web search. When searching for email, people know many attributes about what they are looking for; they often look for specific known items; their queries are shorter and they click on fewer items than in web search. Although repeat queries are common in both email and web search, repeat visits to the same search result are much less common in email search suggesting that the same query is used for different search intents over time. We consider search intent from multiple angles. In email search logs, we find that people use email search not just to find information but also to perform tasks such as cleanup or organization, and that the distribution of actions they perform depends on the type of query. In our survey, people reported that they looked for specific information in both email search and web search, but they were much less likely to search for general information on a topic in email. The differences in overall behavior, re-finding patterns and search intents we observed between email and web search have important implications for the design of email search algorithms and interfaces.