Web search strategies: The influence of Web experience and task type

Thatcher, Andrew

doi:10.1016/j.ipm.2007.09.004

Cited by 110 publications

(83 citation statements)

References 37 publications

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“…Ford et al [13] showed that a variety of individual differences including cognitive style, search experience and age influence both search strategies and task outcome. Bhavnani [4] and Thatcher [33] examined how search behavior varies with domain and search expertise. Bhavnani showed that domain knowledge influences the choice of search strategies and search success.…”

Section: Related Workmentioning

confidence: 99%

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

Buscher

White

Dumais

et al. 2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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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.

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Section: Related Workmentioning

confidence: 99%

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

Buscher

White

Dumais

et al. 2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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“…Thatcher et al observed users opening multiple browser windows to simultaneously conduct multiple searches [15]. A survey of experienced Web users by Aula et al [2] found that multiple windows and tabs were used to support backtracking or multitasking while one page was loading.…”

Section: Related Workmentioning

confidence: 99%

Parallel browsing behavior on the web

Huang

White

2010

Proceedings of the 21st ACM Conference on Hypertext and Hypermedia

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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.

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“…Also, experts were found to navigate search interfaces more efficiently. Beyond search skills, Thatcher [31] showed that experts and non-experts followed different strategies to obtain search results, depending on the task. White and Morris [34] conducted a large-scale log analysis of the differences in search behaviors and success of search experts and novices.…”

Section: Expertisementioning

confidence: 99%

Personalizing atypical web search sessions

Eickhoff

Collins-Thompson

Bennett

et al. 2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

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Most research in Web search personalization models users as static or slowly evolving entities with a given set of preferences defined by their past behavior. However, recent publications as well as empirical evidence suggest that for a significant number of search sessions, users diverge from their regular search profiles in order to satisfy atypical, limitedduration information needs. In this work, we conduct a large-scale inspection of real-life search sessions to further understand this scenario. Subsequently, we design an automatic means of detecting and supporting such atypical sessions. We demonstrate significant improvements over stateof-the-art Web search personalization techniques by accounting for the typicality of search sessions. The proposed method is evaluated based on Web-scale search session data spanning several months of user activity.

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Web search strategies: The influence of Web experience and task type

Cited by 110 publications

References 37 publications

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

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

Parallel browsing behavior on the web

Personalizing atypical web search sessions

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