Publication statistics

Pub. period:2002-2012
Pub. count:59
Number of co-authors:62



Co-authors

Number of publications with 3 favourite co-authors:

Ian Ruthven:8
Joemon M. Jose:7
Matthew Richardson:6

 

 

Productive colleagues

Ryen W. White's 3 most productive colleagues in number of publications:

Alan J. Dix:107
Alan Dix:107
Gary Marchionini:74
 
 
 

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Ryen W. White

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Publications by Ryen W. White (bibliography)

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2012
 
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Bennett, Paul N., White, Ryen W., Chu, Wei, Dumais, Susan T., Bailey, Peter, Borisyuk, Fedor and Cui, Xiaoyuan (2012): Modeling the impact of short- and long-term behavior on search personalization. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 185-194. Available online

User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization. Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone. We also characterize how the relative contribution of each model changes throughout the duration of a session. Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience.

© All rights reserved Bennett et al. and/or ACM Press

 
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Huang, Jeff, White, Ryen W., Buscher, Georg and Wang, Kuansan (2012): Improving searcher models using mouse cursor activity. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 195-204. Available online

Web search components such as ranking and query suggestions analyze the user data provided in query and click logs. While this data is easy to collect and provides information about user behavior, it omits user interactions with the search engine that do not hit the server; these logs omit search data such as users' cursor movements. Just as clicks provide signals for relevance in search results, cursor hovering and scrolling can be additional implicit signals. In this work, we demonstrate a technique to extend models of the user's search result examination state to infer document relevance. We start by exploring recorded user interactions with the search results, both qualitatively and quantitatively. We find that cursor hovering and scrolling are signals telling us which search results were examined, and we use these interactions to reveal latent variables in searcher models to more accurately compute document attractiveness and satisfaction. Accuracy is evaluated by computing how well our model using these parameters can predict future clicks for a particular query. We are able to improve the click predictions compared to a basic searcher model for higher ranked search results using the additional log data.

© All rights reserved Huang et al. and/or ACM Press

 
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White, Ryen W. and Horvitz, Eric (2012): Studies of the onset and persistence of medical concerns in search logs. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 265-274. Available online

The Web provides a wealth of information about medical symptoms and disorders. Although this content is often valuable to consumers, studies have found that interaction with Web content may heighten anxiety and stimulate healthcare utilization. We present a longitudinal log-based study of medical search and browsing behavior on the Web. We characterize how users focus on particular medical concerns and how concerns persist and influence future behavior, including changes in focus of attention in searching and browsing for health information. We build and evaluate models that predict transitions from searches on symptoms to searches on health conditions, and escalations from symptoms to serious illnesses. We study the influence that the prior onset of concerns may have on future behavior, including sudden shifts back to searching on the concern amidst other searches. Our findings have implications for refining Web search and retrieval to support people pursuing diagnostic information.

© All rights reserved White and Horvitz and/or ACM Press

 
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Agichtein, Eugene, White, Ryen W., Dumais, Susan T. and Bennet, Paul N. (2012): Search, interrupted: understanding and predicting search task continuation. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 315-324. Available online

Many important search tasks require multiple search sessions to complete. Tasks such as travel planning, large purchases, or job searches can span hours, days, or even weeks. Inevitably, life interferes, requiring the searcher either to recover the "state" of the search manually (most common), or plan for interruption in advance (unlikely). The goal of this work is to better understand, characterize, and automatically detect search tasks that will be continued in the near future. To this end, we analyze a query log from the Bing Web search engine to identify the types of intents, topics, and search behavior patterns associated with long-running tasks that are likely to be continued. Using our insights, we develop an effective prediction algorithm that significantly outperforms both the previous state-of-the-art method, and even the ability of human judges, to predict future task continuation. Potential applications of our techniques would allow a search engine to preemptively "save state" for a searcher (e.g., by caching search results), perform more targeted personalization, and otherwise better support the searcher experience for interrupted search tasks.

© All rights reserved Agichtein et al. and/or ACM Press

 
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Liebling, Daniel J., Bennett, Paul N. and White, Ryen W. (2012): Anticipatory search: using context to initiate search. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1035-1036. Available online

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.

© All rights reserved Liebling et al. and/or ACM Press

 
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White, Ryen W. and Richardson, Matthew (2012): Effects of expertise differences in synchronous social Q&A. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1055-1056. Available online

Synchronous social question-and-answer (Q&A) systems match askers to answerers and support real-time dialog between them to resolve questions. These systems typically find answerers based on the degree of expertise match with the asker's initial question. However, since synchronous social Q&A involves a dialog between asker and answerer, differences in expertise may also matter (e.g., extreme novices and experts may have difficulty establishing common ground). In this poster we use data from a live social Q&A system to explore the impact of expertise differences on answer quality and aspects of the dialog itself. The findings of our study suggest that synchronous social Q&A systems should consider the relative expertise of candidate answerers with respect to the asker, and offer interactive dialog support to help establish common ground between askers and answerers.

© All rights reserved White and Richardson and/or ACM Press

 
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White, Ryen W. and Buscher, Georg (2012): Text selections as implicit relevance feedback. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1151-1152. Available online

Users' search activity has been used as implicit feedback to model search interests and improve the performance of search systems. In search engines, this behavior usually takes the form of queries and result clicks. However, richer data on how people engage with search results can now be captured at scale, creating new opportunities to enhance search. In this poster we focus on one type of newly-observable behavior: text selection events on search-result captions. We show that we can use text selections as implicit feedback to significantly improve search result relevance.

© All rights reserved White and Buscher and/or ACM Press

2011
 
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Richardson, Matthew and White, Ryen W. (2011): Supporting synchronous social Q&A throughout the question lifecycle. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 755-764. Available online

Synchronous social Q&A systems exist on the Web and in the enterprise to connect people with questions to people with answers in real-time. In such systems, askers' desire for quick answers is in tension with costs associated with interrupting numerous candidate answerers per question. Supporting users of synchronous social Q&A systems at various points in the question lifecycle (from conception to answer) helps askers make informed decisions about the likelihood of question success and helps answerers face fewer interruptions. For example, predicting that a question will not be well answered may lead the asker to rephrase or retract the question. Similarly, predicting that an answer is not forthcoming during the dialog can prompt system behaviors such as finding other answerers to join the conversation. As another example, predictions of asker satisfaction can be assigned to completed conversations and used for later retrieval. In this paper, we use data from an instant-messaging-based synchronous social Q&A service deployed to an online community of over two thousand users to study the prediction of: (i) whether a question will be answered, (ii) the number of candidate answerers that the question will be sent to, and (iii) whether the asker will be satisfied by the answer received. Predictions are made at many points of the question lifecycle (e.g., when the question is entered, when the answerer is located, halfway through the asker-answerer dialog, etc.). The findings from our study show that we can learn capable models for these tasks using a broad range of features derived from user profiles, system interactions, question setting, and the dialog between asker and answerer. Our research can lead to more sophisticated and more useful real-time Q&A support.

© All rights reserved Richardson and White and/or ACM Press

 
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White, Ryen W. and Singla, Adish (2011): Finding our way on the web: exploring the role of waypoints in search interaction. In: Proceedings of the 2011 International Conference on the World Wide Web 2011. pp. 147-148. Available online

Information needs are rarely satisfied directly on search engine result pages. Searchers usually need to click through to search results (landing pages) and follow search trails beyond those pages to fulfill information needs. We use the term waypoints to describe pages visited by searchers between the trail origin (the landing page) and the trail destination. The role that waypoints play in search interaction is poorly understood yet can be vital in determining search success. In this poster we analyze log data to determine the arrangement and function of waypoints, and study how these are affected by variations in information goals. Our findings have implications for understanding search behavior and for the design of interactive search support based on waypoints.

© All rights reserved White and Singla and/or ACM Press

 
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Huang, Jeff, White, Ryen W. and Dumais, Susan (2011): No clicks, no problem: using cursor movements to understand and improve search. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1225-1234. Available online

Understanding how people interact with search engines is important in improving search quality. Web search engines typically analyze queries and clicked results, but these actions provide limited signals regarding search interaction. Laboratory studies often use richer methods such as gaze tracking, but this is impractical at Web scale. In this paper, we examine mouse cursor behavior on search engine results pages (SERPs), including not only clicks but also cursor movements and hovers over different page regions. We: (i) report an eye-tracking study showing that cursor position is closely related to eye gaze, especially on SERPs; (ii) present a scalable approach to capture cursor movements, and an analysis of search result examination behavior evident in these large-scale cursor data; and (iii) describe two applications (estimating search result relevance and distinguishing good from bad abandonment) that demonstrate the value of capturing cursor data. Our findings help us better understand how searchers use cursors on SERPs and can help design more effective search systems. Our scalable cursor tracking method may also be useful in non-search settings.

© All rights reserved Huang et al. and/or their publisher

 
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White, Ryen W., Richardson, Matthew and Liu, Yandong (2011): Effects of community size and contact rate in synchronous social q&a. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 2837-2846. Available online

Social question-and-answer (Q&A) involves the location of answers to questions through communication with people. Social Q&A systems, such as mailing lists and Web forums are popular, but their asynchronous nature can lead to high answer latency. Synchronous Q&A systems facilitate real-time dialog, usually via instant messaging, but face challenges with interruption costs and the availability of knowledgeable answerers at question time. We ran a longitudinal study of a synchronous social Q&A system to investigate the effects of the rate with which potential answerers were contacted (trading off time-to-answer against interruption cost) and community size (varying total number of members). We found important differences in subjective and objective measures of system performance with these variations. Our findings help us understand the costs and benefits of varying contact rate and community size in synchronous social Q&A, and inform system design for social Q&A.

© All rights reserved White et al. and/or their publisher

 
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Kotov, Alexander, Bennett, Paul N., White, Ryen W., Dumais, Susan T. and Teevan, Jaime (2011): Modeling and analysis of cross-session search tasks. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 5-14. Available online

The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries issued over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span session boundaries. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all of the related queries from previous sessions that the same user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze cross-session search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks.

© All rights reserved Kotov et al. and/or ACM Press

 
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Cartright, Marc-Allen, White, Ryen W. and Horvitz, Eric (2011): Intentions and attention in exploratory health search. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 65-74. Available online

We study information goals and patterns of attention in exploratory search for health information on the Web, reporting results of a large-scale log-based study. We examine search activity associated with the goal of diagnosing illness from symptoms versus more general information-seeking about health and illness. We decompose exploratory health search into evidence-based and hypothesis-directed information seeking. Evidence-based search centers on the pursuit of details and relevance of signs and symptoms. Hypothesis-directed search includes the pursuit of content on one or more illnesses, including risk factors, treatments, and therapies for illnesses, and on the discrimination among different diseases under the uncertainty that exists in advance of a confirmed diagnosis. These different goals of exploratory health search are not independent, and transitions can occur between them within or across search sessions. We construct a classifier that identifies medically-related search sessions in log data. Given a set of search sessions flagged as health-related, we show how we can identify different intentions persisting as foci of attention within those sessions. Finally, we discuss how insights about foci dynamics can help us better understand exploratory health search behavior and better support health search on the Web.

© All rights reserved Cartright et al. and/or ACM Press

 
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Bennett, Paul N., Radlinski, Filip, White, Ryen W. and Yilmaz, Emine (2011): Inferring and using location metadata to personalize web search. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 135-144. Available online

Personalization of search results offers the potential for significant improvements in Web search. Among the many observable user attributes, approximate user location is particularly simple for search engines to obtain and allows personalization even for a first-time Web search user. However, acting on user location information is difficult, since few Web documents include an address that can be interpreted as constraining the locations where the document is relevant. Furthermore, many Web documents -- such as local news stories, lottery results, and sports team fan pages -- may not correspond to physical addresses, but the location of the user still plays an important role in document relevance. In this paper, we show how to infer a more general location relevance which uses not only physical location but a more general notion of locations of interest for Web pages. We compute this information using implicit user behavioral data, characterize the most location-centric pages, and show how location information can be incorporated into Web search ranking. Our results show that a substantial fraction of Web search queries can be significantly improved by incorporating location-based features.

© All rights reserved Bennett et al. and/or ACM Press

 
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Guo, Qi, White, Ryen W., Zhang, Yunqiao, Anderson, Blake and Dumais, Susan T. (2011): Why searchers switch: understanding and predicting engine switching rationales. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 335-344. Available online

Search engine switching is the voluntary transition between Web search engines. Engine switching can occur for a number of reasons, including user dissatisfaction with search results, a desire for broader topic coverage or verification, user preferences, or even unintentionally. An improved understanding of switching rationales allows search providers to tailor the search experience according to the different causes. In this paper we study the reasons behind search engine switching within a session. We address the challenge of identifying switching rationales by designing and implementing client-side instrumentation to acquire in-situ feedbacks from users. Using this feedback, we investigate in detail the reasons that users switch engines within a session. We also study the relationship between implicit behavioral signals and the switching causes, and develop and evaluate models to predict the reasons for switching. In addition, we collect editorial judgments of switching rationales by third-party judges and show that we can recover switching causes a posteriori. Our findings provide valuable insights into why users switch search engines in a session and demonstrate the relationship between search behavior and switching motivations. The findings also reveal sufficient behavioral consistency to afford accurate prediction of switching rationale, which can be used to dynamically adapt the search experience and derive more accurate competitive metrics.

© All rights reserved Guo et al. and/or ACM Press

 
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Tang, Jie, White, Ryen W. and Bailey, Peter (2011): Recommending interesting activity-related local entities. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1161-1162. Available online

When searching for entities with a strong local character (e.g., a museum), people may also be interested in discovering proximal activity-related entities (e.g., a caf). Geographical proximity is a necessary, but not sufficient, qualifier for recommending other entities such that they are related in a useful manner (e.g., interest in a fish market does not imply interest in nearby bookshops, but interest in other produce stores is more likely). We describe and evaluate methods to identify such activity-related local entities.

© All rights reserved Tang et al. and/or ACM Press

 
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Andr, Paul, Schraefel, M. C., Dix, Alan and White, Ryen W. (2011): Expressing well-being online: towards self-reflection and social awareness. In: Proceedings of the 2011 iConference 2011. pp. 114-121. Available online

Medicine, psychology and quality of life literature all point to the importance of not just asking 'how are you?', but assessing and being aware of self and others' well-being. Social networking has been shown to have a variety of uses and benefits, but does not currently offer explicit expression of a well-being state. We developed and deployed Healthii, a social networking tool to convey well-being using a set of pre-defined discrete categories. We sought to understand how communicating this in a lightweight fashion may be used and valued. Using a hybrid methodology, over five weeks ten participants used the tool on Facebook, Twitter, or on the desktop, and in group meetings discussed the affect and effect of the tool, before a final individual survey. The trial showed that participants used and valued status expression for its support to convey state, and for self-reflection and group awareness. We discuss these findings as well as future opportunities for awareness visualization and automatic data integration.

© All rights reserved Andr et al. and/or ACM Press

2010
 
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Andr, Paul, schraefel, m.c., Dix, Alan J. and White, Ryen W. (2010): Experience in social affective applications: methodologies and case study. In: Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems 2010. pp. 2755-2764. Available online

New forms of social affective applications are emerging, bringing with them challenges in design and evaluation. We report on one such application, conveying well-being for both personal and group benefit, and consider why existing methodologies may not be suitable, before explaining and analyzing our proposed approach. We discuss our experience of using and writing about the methodology, in order to invite discussion about its suitability in particular, as well as the more general need for methodologies to examine experience and affect in social, connected situations. As these fields continue to interact, we hope that these discussions serve to aid in studying and learning from these types of application.

© All rights reserved Andr et al. and/or their publisher

 
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Huang, Jeff and White, Ryen W. (2010): Parallel browsing behavior on the web. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia 2010. pp. 13-18. Available online

Parallel browsing describes a behavior where users visit Web pages in multiple concurrent threads. Web browsers explicitly support this by providing tabs. Although parallel browsing is more prevalent than linear browsing online, little is known about how users perform this activity. We study the use of parallel browsing through a log-based study of millions of Web users and present findings on their behavior. We identify a power law distribution in browser metrics comprising "outclicks" and tab switches, which signify the degree of parallel browsing. We find that users switch tabs at least 57.4% of the time, but user activity, measured in pageviews, is split among tabs rather than increasing overall activity. Finally, analysis of a subset of the logs focused on Web search shows that while the majority of users do not branch from search engine result pages, the degree of branching is higher for non-navigational queries. Our findings have design implications for Web sites and browsers, search interfaces, and log analysis.

© All rights reserved Huang and White and/or their publisher

 
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Liu, Chao, White, Ryen W. and Dumais, Susan (2010): Understanding web browsing behaviors through Weibull analysis of dwell time. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 379-386. Available online

Dwell time on Web pages has been extensively used for various information retrieval tasks. However, some basic yet important questions have not been sufficiently addressed, eg, what distribution is appropriate to model the distribution of dwell times on a Web page, and furthermore, what the distribution tells us about the underlying browsing behaviors. In this paper, we draw an analogy between abandoning a page during Web browsing and a system failure in reliability analysis, and propose to model the dwell time using the Weibull distribution. Using this distribution provides better goodness-of-fit to real world data, and it uncovers some interesting patterns of user browsing behaviors not previously reported. For example, our analysis reveals that Web browsing in general exhibits a significant "negative aging" phenomenon, which means that some initial screening has to be passed before a page is examined in detail, giving rise to the browsing behavior that we call "screen-and-glean." In addition, we demonstrate that dwell time distributions can be reasonably predicted purely based on low-level page features, which broadens the possible applications of this study to situations where log data may be unavailable.

© All rights reserved Liu et al. and/or their publisher

 
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White, Ryen W. and Huang, Jeff (2010): Assessing the scenic route: measuring the value of search trails in web logs. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 587-594. Available online

Search trails mined from browser or toolbar logs comprise queries and the post-query pages that users visit. Implicit endorsements from many trails can be useful for search result ranking, where the presence of a page on a trail increases its query relevance. Following a search trail requires user effort, yet little is known about the benefit that users obtain from this activity versus, say, sticking with the clicked search result or jumping directly to the destination page at the end of the trail. In this paper, we present a log-based study estimating the user value of trail following. We compare the relevance, topic coverage, topic diversity, novelty, and utility of full trails over that provided by sub-trails, trail origins (landing pages), and trail destinations (pages where trails end). Our findings demonstrate significant value to users in following trails, especially for certain query types. The findings have implications for the design of search systems, including trail recommendation systems that display trails on search result pages.

© All rights reserved White and Huang and/or their publisher

 
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Bailey, Peter, Craswell, Nick, White, Ryen W., Chen, Liwei, Satyanarayana, Ashwin and Tahaghoghi, S. M. M. (2010): Evaluating whole-page relevance. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 767-768. Available online

Whole page relevance defines how well the surface-level representation of all elements on a search result page and the corresponding holistic attributes of the presentation respond to users' information needs. We introduce a method for evaluating the whole-page relevance of Web search engine results pages. Our key contribution is that the method allows us to investigate aspects of component relevance that are difficult or impossible to judge in isolation. Such aspects include component-level information redundancy and cross-component coherence. The method we describe complements traditional document relevance measurement, affords comparative relevance assessment across multiple search engines, and facilitates the study of important factors such as brand presentation effects and component-level quality.

© All rights reserved Bailey et al. and/or their publisher

 
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White, Ryen W. and Horvitz, Eric (2010): Predicting escalations of medical queries based on web page structure and content. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 769-770. Available online

Logs of users' searches on Web health topics can exhibit signs of escalation of medical concerns, where initial queries about common symptoms are followed by queries about serious, rare illnesses. We present an effort to predict such escalations based on the structure and content of pages encountered during medical search sessions. We construct and then characterize the performance of classifiers that predict whether an escalation will occur after the access of a page. Our findings have implications for ranking algorithms and the design of search interfaces.

© All rights reserved White and Horvitz and/or their publisher

 
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White, Ryen W. and Chandrasekar, Raman (2010): Exploring the use of labels to shortcut search trails. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 811-812. Available online

Search trails comprising queries and Web page views are created as searchers engage in information-seeking activity online. During known-item search (where the objective may be to locate a target Web page), searchers may waste valuable time repeatedly reformulating queries as they attempt to locate an elusive page. Trail shortcuts help users bypass unnecessary queries and get them to their desired destination faster. In this poster we present a comparative oracle study of techniques to shortcut sub-optimal search trails using labels derived from social bookmarking, anchor text, query logs, and a human-computation game. We show that labels can help users reach target pages efficiently, that the label sources perform differently, and that shortcuts are potentially most useful when the target is challenging to find.

© All rights reserved White and Chandrasekar and/or their publisher

 
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Singla, Adish and White, Ryen W. (2010): Sampling high-quality clicks from noisy click data. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 1187-1188. Available online

Click data captures many users' document preferences for a query and has been shown to help significantly improve search engine ranking. However, most click data is noisy and of low frequency, with queries associated to documents via only one or a few clicks. This severely limits the usefulness of click data as a ranking signal. Given potentially noisy clicks comprising results with at most one click for a query, how do we extract high-quality clicks that may be useful for ranking? In this poster, we introduce a technique based on query entropy for noise reduction in click data. We study the effect of query entropy and as well as features such as user engagement and the match between the query and the document. Based on query entropy plus other features, we can sample noisy data to 15% of its overall size with 43% query recall and an average increase of 20% in precision for recalled queries.

© All rights reserved Singla and White and/or their publisher

 
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White, Ryen W., Kapoor, Ashish and Dumais, Susan T. (2010): Modeling Long-Term Search Engine Usage. In: Proceedings of the 2010 Conference on User Modeling, Adaptation and Personalization 2010. pp. 28-39. Available online

Search engines are key components in the online world and the choice of search engine is an important determinant of the user experience. In this work we seek to model user behaviors and determine key variables that affect search engine usage. In particular, we study the engine usage behavior of more than ten thousand users over a period of six months and use machine learning techniques to identify key trends in the usage of search engines and their relationship with user satisfaction. We also explore methods to determine indicators that are predictive of user trends and show that accurate predictive user models of search engine usage can be developed. Our findings have implications for users as well as search engine designers and marketers seeking to better understand and retain their users.

© All rights reserved White et al. and/or their publisher

 
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Bailey, Peter, White, Ryen W., Liu, Han and Kumaran, Giridhar (2010): Mining Historic Query Trails to Label Long and Rare Search Engine Queries. In ACM Transactions on the Web, 4 (4) p. 15. Available online

Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment of labels to long queries can capture aspects of a user's search intent that may not be apparent from the terms in the query. This affords search result matching or reranking based on queries and labels rather than the query text alone. Query labels can be derived from interaction logs generated from many users" search result clicks or from query trails comprising the chain of URLs visited following query submission. However, since long queries are typically rare, they are difficult to label in this way because little or no historic log data exists for them. A subset of these queries may be amenable to labeling by detecting similarities between parts of a long and rare query and the queries which appear in logs. In this article, we present the comparison of four similarity algorithms for the automatic assignment of Open Directory Project category labels to long and rare queries, based solely on matching against similar satisfied query trails extracted from log data. Our findings show that although the similarity-matching algorithms we investigated have tradeoffs in terms of coverage and accuracy, one algorithm that bases similarity on a popular search result ranking function (effectively regarding potentially-similar queries as "documents") outperforms the others. We find that it is possible to correctly predict the top label better than one in five times, even when no past query trail exactly matches the long and rare query. We show that these labels can be used to reorder top-ranked search results leading to a significant improvement in retrieval performance over baselines that do not utilize query labeling, but instead rank results using content-matching or click-through logs. The outcomes of our research have implications for search providers attempting to provide users with highly-relevant search results for long queries.

© All rights reserved Bailey et al. and/or ACM

2009
 
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schraefel, m.c., White, Ryen W., Andr, Paul and Tan, Desney (2009): Investigating web search strategies and forum use to support diet and weight loss. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 3829-3834. Available online

Healthcare is shifting from being reactive to preventive, with a focus on maintaining general wellness through positive decisions on diet, exercise, and lifestyle. In this paper, we investigate search behavior as people navigate the Web and find support for dietary and weight loss plans. Inspecting the Web search logs of nearly 2,000 users, we show that people progressively narrow their searches to support their progress through these plans. Interestingly, people that visit online health forums seem to progress through the plans' phases more quickly. Based on these results, we conducted a survey to further explore the roles and importance of online forums in supporting dieting and weight loss.

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White, Ryen W., Bailey, Peter and Chen, Liwei (2009): Predicting user interests from contextual information. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2009. pp. 363-370. Available online

Search and recommendation systems must include contextual information to effectively model users' interests. In this paper, we present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. Post-query navigation and general browsing behaviors far outweigh direct search engine interaction as an information-gathering activity. Therefore we conducted this study with a focus on Website recommendations rather than search results. The five contextual information sources used are: social, historic, task, collection, and user interaction. We evaluate the utility of these sources, and overlaps between them, based on how effectively they predict users' future interests. Our findings demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source. Designers of Website suggestion systems can use our findings to provide improved support for post-query navigation and general browsing behaviors.

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Macdonald, Craig and White, Ryen W. (2009): Usefulness of click-through data in expert search. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2009. pp. 816-817. Available online

The task in expert finding is to identify members of an organisation with relevant expertise on a given topic. Typically, an expert search engine uses evidence from the authors of on-topic documents found in the organisation's intranet by search engines. The search result click-through behaviour of many intranet search engine users provides an additional source of evidence to identify topically-relevant documents, and via document authorship, experts. In this poster, we assess the usefulness of click-through log data for expert finding. We find that ranking authors based solely on the clicks their documents receive is reasonably effective at correctly identifying relevant experts. Moreover, we show that this evidence can successfully be integrated with an existing expert search engine to increase its retrieval effectiveness.

© All rights reserved Macdonald and White and/or their publisher

 
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White, Ryen W. and Horvitz, Eric (2009): Cyberchondria: Studies of the escalation of medical concerns in Web search. In ACM Transactions on Information Systems, 27 (4) p. 23. Available online

The World Wide Web provides an abundant source of medical information. This information can assist people who are not healthcare professionals to better understand health and illness, and to provide them with feasible explanations for symptoms. However, the Web has the potential to increase the anxieties of people who have little or no medical training, especially when Web search is employed as a diagnostic procedure. We use the term cyberchondria to refer to the unfounded escalation of concerns about common symptomatology, based on the review of search results and literature on the Web. We performed a large-scale, longitudinal, log-based study of how people search for medical information online, supported by a survey of 515 individuals' health-related search experiences. We focused on the extent to which common, likely innocuous symptoms can escalate into the review of content on serious, rare conditions that are linked to the common symptoms. Our results show that Web search engines have the potential to escalate medical concerns. We show that escalation is associated with the amount and distribution of medical content viewed by users, the presence of escalatory terminology in pages visited, and a user's predisposition to escalate versus to seek more reasonable explanations for ailments. We also demonstrate the persistence of postsession anxiety following escalations and the effect that such anxieties can have on interrupting user's activities across multiple sessions. Our findings underscore the potential costs and challenges of cyberchondria and suggest actionable design implications that hold opportunity for improving the search and navigation experience for people turning to the Web to interpret common symptoms.

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Karimzadehgan, Maryam, White, Ryen W. and Richardson, Matthew (2009): Enhancing Expert Finding Using Organizational Hierarchies. In: Boughanem, Mohand, Berrut, Catherine, Mothe, Josiane and Soul-Dupuy, Chantal (eds.) Advances in Information Retrieval - 31th European Conference on IR Research - ECIR 2009 April 6-9, 2009, 2009, Toulouse, France. pp. 177-188. Available online

2008
 
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White, Ryen W., Richardson, Matthew, Bilenko, Mikhail and Heath, Allison P. (2008): Enhancing web search by promoting multiple search engine use. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 43-50. Available online

Any given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users' best interest to utilize multiple search engines. In this paper, we propose and evaluate a framework that maximizes users' search effective-ness by directing them to the engine that yields the best results for the current query. In contrast to prior work on meta-search, we do not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. We describe a machine learning approach to supporting switching between search engines and demonstrate its viability at tolerable interruption levels. Our findings have implications for fluid competition between search engines.

© All rights reserved White et al. and/or ACM Press

 
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Bilenko, Mikhail, White, Ryen W., Richardson, Matthew and Murray, G. Craig (2008): Talking the talk vs. walking the walk: salience of information needs in querying vs. browsing. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 705-706. Available online

Traditional information retrieval models assume that users express their information needs via text queries (i.e., their "talk"). In this poster, we consider Web browsing behavior outside of interactions with retrieval systems (i.e., users' "walk") as an alternative source of signal describing users' information needs, and compare it to the query-expressed information needs on a large dataset. Our findings demonstrate that information needs expressed in different behavior modalities are largely non-overlapping, and that past behavior in each modality is the most accurate predictor of future behavior in that modality. Results also show that browsing data provides a stronger source of signal than search queries due to its greater volume, which explains previous work that has found implicit behavioral data to be a valuable source of information for user modeling and personalization.

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White, Ryen W., Dumais, Susan and Teevan, Jaime (2008): How medical expertise influences web search interaction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 791-792. Available online

Domain expertise can have an important influence on how people search. In this poster we present findings from a log-based study into how medical domain experts search the Web for information related to their expertise, as compared with non-experts. We find differences in sites visited, query vocabulary, and search behavior. The findings have implications for the automatic identification of domain experts from interaction logs, and the use of domain knowledge in applications such as query suggestion or page recommendation to support non-experts.

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Bilenko, Mikhail and White, Ryen W. (2008): Mining the search trails of surfing crowds: identifying relevant websites from user activity. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 51-60. Available online

The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.

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Heath, Allison P. and White, Ryen W. (2008): Defection detection: predicting search engine switching. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1173-1174. Available online

Searchers have a choice about which Web search engine they use when looking for information online. If they are unsuccessful on one engine, users may switch to a different engine to continue their search. By predicting when switches are likely to occur, the search experience can be modified to retain searchers or ensure a quality experience for incoming searchers. In this poster, we present research on a technique for predicting search engine switches. Our findings show that prediction is possible at a reasonable level of accuracy, particularly when personalization or user grouping is employed. These findings have implications for the design of applications to support more effective online searching.

© All rights reserved Heath and White and/or ACM Press

 
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Macdonald, Craig, Ounis, Iadh, Plachouras, Vassilis, Ruthven, Ian and White, Ryen W. (eds.) Advances in Information Retrieval - 30th European Conference on IR Research - ECIR 2008 March 30-April 3, 2008, Glasgow, UK.

 
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White, Ryen W., Bilenko, Mikhail and Cucerzan, Silviu (2008): Leveraging popular destinations to enhance Web search interaction. In ACM Transactions on the Web, 2 (3) p. 16. Available online

This article presents a novel Web search interaction feature that for a given query provides links to Web sites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the query topic. Destinations are identified using the history of the search and browsing behavior of many users over an extended time period, and their collective behavior provides a basis for computing source authority. They are drawn from the end of users' postquery browse trails where users may cease searching once they find relevant information. We describe a user study that compared the suggestion of destinations with the previously proposed suggestion of related queries as well as with traditional, unaided Web search. Results show that search enhanced by query suggestions outperforms other systems in terms of subject perceptions and search effectiveness for fact-finding search tasks. However, search enhanced by destination suggestions performs best for exploratory tasks with its best performance obtained from mining past user behavior at query-level granularity. We discuss the implications of these and other findings from our study for the design of search systems that utilize user behavior, in particular, user browse trails and popular destinations.

© All rights reserved White et al. and/or ACM

2007
 
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White, Ryen W. and Drucker, Steven M. (2007): Investigating behavioral variability in web search. In: Proceedings of the 2007 International Conference on the World Wide Web 2007. pp. 21-30. Available online

Understanding the extent to which people's search behaviors differ in terms of the interaction flow and information targeted is important in designing interfaces to help World Wide Web users search more effectively. In this paper we describe a longitudinal log-based study that investigated variability in people's interaction behavior when engaged in search-related activities on the Web. We analyze the search interactions of more than two thousand volunteer users over a five-month period, with the aim of characterizing differences in their interaction styles. The findings of our study suggest that there are dramatic differences in variability in key aspects of the interaction within and between users, and within and between the search queries they submit. Our findings also suggest two classes of extreme user. navigators and explorers. whose search interaction is highly consistent or highly variable. Lessons learned from these users can inform the design of tools to support effective Web-search interactions for everyone.

© All rights reserved White and Drucker and/or International World Wide Web Conference Committee

 
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Clarke, Charles L. A., Agichtein, Eugene, Dumais, Susan and White, Ryen W. (2007): The influence of caption features on clickthrough patterns in web search. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 135-142. Available online

Web search engines present lists of captions, comprising title, snippet, and URL, to help users decide which search results to visit. Understanding the influence of features of these captions on Web search behavior may help validate algorithms and guidelines for their improved generation. In this paper we develop a methodology to use clickthrough logs from a commercial search engine to study user behavior when interacting with search result captions. The findings of our study suggest that relatively simple caption features such as the presence of all terms query terms, the readability of the snippet, and the length of the URL shown in the caption, can significantly influence users' Web search behavior.

© All rights reserved Clarke et al. and/or ACM Press

 
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White, Ryen W., Bilenko, Mikhail and Cucerzan, Silviu (2007): Studying the use of popular destinations to enhance web search interaction. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 159-166. Available online

We present a novel Web search interaction feature which, for a given query, provides links to websites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the query topic. Destinations are identified using the history of search and browsing behavior of many users over an extended time period, whose collective behavior provides a basis for computing source authority. We describe a user study which compared the suggestion of destinations with the previously proposed suggestion of related queries, as well as with traditional, unaided Web search. Results show that search enhanced by destination suggestions outperforms other systems for exploratory tasks, with best performance obtained from mining past user behavior at query-level granularity.

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White, Ryen W. and Morris, Dan (2007): Investigating the querying and browsing behavior of advanced search engine users. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 255-262. Available online

One way to help all users of commercial Web search engines be more successful in their searches is to better understand what those users with greater search expertise are doing, and use this knowledge to benefit everyone. In this paper we study the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, result clicks, post-query browsing, and search success of users we classify as advanced (based on their use of query operators), relative to those classified as non-advanced. Our findings have implications for how advanced users should be supported during their searches, and how their interactions could be used to help searchers of all experience levels find more relevant information and learn improved searching strategies.

© All rights reserved White and Morris and/or ACM Press

 
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White, Ryen W., Clarke, Charles L. A. and Cucerzan, Silviu (2007): Comparing query logs and pseudo-relevance feedback for web-search query refinement. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 831-832. Available online

Query logs and pseudo-relevance feedback (PRF) offer ways in which terms to refine Web searchers' queries can be selected, offered to searchers, and used to improve search effectiveness. In this poster we present a study of these techniques that aims to characterize the degree of similarity between them across a set of test queries, and the same set broken out by query type. The results suggest that: (i) similarity increases with the amount of evidence provided to the PRF algorithm, (ii) similarity is higher when titles/snippets are used for PRF than full-text, and (iii) similarity is higher for navigational than informational queries. The findings have implications for the combined usage of query logs and PRF in generating query refinement alternatives.

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Cucerzan, Silviu and White, Ryen W. (2007): Query suggestion based on user landing pages. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 875-876. Available online

This poster investigates a novel query suggestion technique that selects query refinements through a combination of many users' post-query navigation patterns and the query logs of a large search engine. We compare this technique, which uses the queries that retrieve in the top-ranked search results places where searchers end up after post-query browsing (i.e., the landing pages), with an approach based on query refinements from user search sessions extracted from query logs. Our findings demonstrate the effectiveness of using landing pages for the direct generation of query suggestions, as well as the complementary nature of the suggestions it generates with regard to traditional query log based refinement methodologies.

© All rights reserved Cucerzan and White and/or ACM Press

 
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Melucci, Massimo and White, Ryen W. (2007): Utilizing a geometry of context for enhanced implicit feedback. In: Silva, Mario J., Laender, Alberto H. F., Baeza-Yates, Ricardo A., McGuinness, Deborah L., Olstad, Bjrn, Olsen, ystein Haug and Falco, Andr O. (eds.) Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management - CIKM 2007 November 6-10, 2007, Lisbon, Portugal. pp. 273-282. Available online

2006
 
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White, Ryen W., Song, Hyunyoung and Liu, Jay (2006): Concept maps to support oral history search and use. In: JCDL06: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries 2006. pp. 192-193. Available online

In this paper we describe a novel technique to support information seeking in oral history archives using concept maps. We conducted a pilot study with teachers engaged in work tasks using a prototype concept mapping tool. Results suggest that concept maps can help searchers, especially when tasks are complex.

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White, Ryen W. and Marchionini, Gary (2006): A study of real-time query expansion effectiveness. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 715-716. Available online

In this poster, we describe the study of an interface technique that provides a list of suggested additional query terms as a searcher types a search query, in effect offering interactive query expansion (IQE) options while the query is formulated. Analysis of the results shows that offering IQE during query formulation leads to better quality initial queries, and an increased uptake of query expansion. These findings have implications for how IQE should be offered in retrieval interfaces.

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White, Ryen W. and Kelly, Diane (2006): A study on the effects of personalization and task information on implicit feedback performance. In: Yu, Philip S., Tsotras, Vassilis J., Fox, Edward A. and Liu, Bing (eds.) Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management November 6-11, 2006, Arlington, Virginia, USA. pp. 297-306. Available online

 
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White, Ryen W. (2006): Information retrieval design: Principles and options for information description, organization, display, and access in information retrieval databases, digital libraries, catalogs, and indexes. In JASIST - Journal of the American Society for Information Science and Technology, 57 (10) pp. 1412-1413. Available online

 
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White, Ryen W. and Ruthven, Ian (2006): A study of interface support mechanisms for interactive information retrieval. In JASIST - Journal of the American Society for Information Science and Technology, 57 (7) pp. 933-948. Available online

 
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White, Ryen W., Kules, Bill, Drucker, Steven M. and Schraefel, Monica M. C. (2006): Introduction. In Communications of the ACM, 49 (4) pp. 36-39. Available online

2005
 
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White, Ryen W., Ruthven, Ian, Jose, Joemon M. and Rijsbergen, C. J. Van (2005): Evaluating implicit feedback models using searcher simulations. In ACM Transactions on Information Systems, 23 (3) pp. 325-361. Available online

In this article we describe an evaluation of relevance feedback (RF) algorithms using searcher simulations. Since these algorithms select additional terms for query modification based on inferences made from searcher interaction, not on relevance information searchers explicitly provide (as in traditional RF), we refer to them as implicit feedback models. We introduce six different models that base their decisions on the interactions of searchers and use different approaches to rank query modification terms. The aim of this article is to determine which of these models should be used to assist searchers in the systems we develop. To evaluate these models we used searcher simulations that afforded us more control over the experimental conditions than experiments with human subjects and allowed complex interaction to be modeled without the need for costly human experimentation. The simulation-based evaluation methodology measures how well the models learn the distribution of terms across relevant documents (i.e., learn what information is relevant) and how well they improve search effectiveness (i.e., create effective search queries). Our findings show that an implicit feedback model based on Jeffrey's rule of conditioning outperformed other models under investigation.

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White, Ryen W., Ruthven, Ian and Jose, Joemon M. (2005): A study of factors affecting the utility of implicit relevance feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 35-42. Available online

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF.

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White, Ryen W., Jose, Joemon M. and Ruthven, Ian (2005): Using top-ranking sentences to facilitate effective information access. In JASIST - Journal of the American Society for Information Science and Technology, 56 (10) pp. 1113-1125. Available online

2004
 
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White, Ryen W. and Jose, Joemon M. (2004): A study of topic similarity measures. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 520-521. Available online

In this poster we describe an investigation of topic similarity measures. We elicit assessments on the similarity of 10 pairs of topic from 76 subjects and use these as a benchmark to assess how well each measure performs. The measures have the potential to form the basis of a predictive technique, for adaptive search systems. The results of our evaluation show that measures based on the level of correlation between topics concords most with general subject perceptions of search topic similarity.

© All rights reserved White and Jose and/or ACM Press

 
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White, Ryen W., Jose, Joemon M., Rijsbergen, C. J. Van and Ruthven, Ian (2004): A Simulated Study of Implicit Feedback Models. In: Mcdonald, Sharon and Tait, John (eds.) Advances in Information Retrieval - 26th European Conference on IR Research - ECIR 2004 April 5-7, 2004, Sunderland, UK. pp. 311-326. Available online

2002
 
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White, Ryen W., Ruthven, Ian and Jose, Joemon M. (2002): Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2002. pp. 57-64. Available online

In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are examined: the presentation of sentences that highly match the searcher's query and the use of implicit evidence. Implicit evidence is evidence captured from the searcher's interaction with the retrieval results and is used to automatically update the display. Our evaluation concentrates on the effectiveness and subject perception of these techniques. The results show, with statistical significance, that the techniques are effective and efficient for information seeking.

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White, Ryen W., Jose, Joemon M. and Ruthven, Ian (2002): A system using implicit feedback and top ranking sentences to help users find relevant web documents. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2002. p. 446. Available online

We present a web search interface designed to encourage users to interact more fully with the results of a web search. Wrapping around a major commercial search engine, the system combines three main features; real-time query-biased web document summarisation, the presentation of sentences highly relevant to the searcher's query, and evidence captured from searcher interaction with the retrieval results.

© All rights reserved White et al. and/or ACM Press

 
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