Investigating behavioral variability in web search

By Ryen W. White, Steven Mark Drucker. The Web Conference, 2007

360 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/79214d896304c1f4bd5e9ef8c7f00b8c34e500b0

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In Information Systems

In Information Systems and Management

Citations in the corpus

Understanding web browsing behaviors through Weibull analysis of dwell time (2010, background): “Although post-query browsing is intuitively more relevant to IR than general browsing, general browsing is still an important component in information seeking [20, 25].”
Click Models for Web Search (2015, background): “3 USERANDQUERYDIVERSITY It is well-known that there is a certain variability between different users and different sessions [White and Drucker, 2007].”
Characterizing the influence of domain expertise on web search behavior (2009, methodology): “This threshold has been used previously to demarcate search sessions in logs [8,20].”
Modeling the impact of short- and long-term behavior on search personalization (2012, methodology): “For our purposes a session is demarcated by 30 minutes of user inactivity as described in [32].”
Identifying Web Search Query Reformulation using Concept based Matching (2013, methodology): “Every record in our data consisted of a consecutive query pair ( Q i , Q i +1 ) submitted to the search engine by the same user and in the same session (i.e. within less than 30 minutes of idle time, the 30 minutes threshold has been frequently used in previous work, e.g. (White and Drucker, 2007)).”
How does search behavior change as search becomes more difficult? (2010, background): “White and Drucker [25] suggested that users are likely to use navigator type systematic behaviors in well-defined fact-finding tasks and explorer-type more variable behavior in complex sense-making tasks.”
Recommending interesting activity-related local entities (2011, methodology): “Search sessions: The frequency of URL co-occurrence in search sessions from one-week of browsing logs, comprising search engine queries and post-query navigation (as described in [5]), is used to compute the degree of relatedness between entities.”
A user browsing model to predict search engine click data from past observations. (2008, background): “User activity models within Web search can be broadly divided in three categories: analysis models where the aim is to gain insight into typical user behavior [13], models that try to predict the next user action [6], and eventually models that estimate the attractiveness or perceived relevance of a”
Exploratory Search: Beyond the Query-Response Paradigm (2009, background): “Evolving or berrypicking searches have been studied in recent years (e.g., Campbell, 1999; Ellis, 1984; Kuhlthau, 1993; Marchionini, 1995; Vakkari and Hakala, 2000; White and Drucker, 2007).”
Information re-retrieval: repeat queries in Yahoo's logs (2007, background): “Fifteen had equalquery queries but no navigational queries, possibly indicating they were explorers [26].”