Information re-retrieval: repeat queries in Yahoo's logs

By Jaime Teevan, Eytan Adar, Rosie Jones, M. A. S. Potts. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

369 citations recorded by the source corpus.

People often repeat Web searches, both to find new information on topics they have previously explored and to re-find information they have seen in the past. The query associated with a repeat search may differ from the initial query but can nonetheless lead to clicks on the same results. This paper explores repeat search behavior through the analysis of a one-year Web query log of 114 anonymous users and a separate controlled survey of an additional 119 volunteers. Our study demonstrates that as many as 40% of all queries are re-finding queries. Re-finding appears to be an important behavior for search engines to explicitly support, and we explore how this can be done. We demonstrate that changes to search engine results can hinder re-finding, and provide a way to automatically detect repeat searches and predict repeat clicks.

Subfields: Computer Science Applications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/6130736464c8e3708fdea3d163e1d34608d2d260

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Citations in the corpus

Analyzing and evaluating query reformulation strategies in web search logs (2009, methodology): “As part of a study of re-finding behavior, Teevan et al. [34] constructed a taxonomy by looking through query logs, and implemented algorithms to detect a subset of the reformulation strategies.”
#TwitterSearch: a comparison of microblog search and web search (2011, background): “Researchers have also used query logs to explore how search engines are used, revealing typical session behavior [15] and the prevalence of re-finding [28].”
The query-flow graph: model and applications (2008, methodology): “Whenever we used a timeout threshold for splitting sessions, we set tθ = 30 minutes, as this is the typical timeout that is often used in web log analysis [7, 21, 18].”
Getting inspired!: understanding how and why examples are used in creative design practice (2009, result): “This is consistent with a recent study showing that forty percent of search queries are attempts to re-find information [ 20 ].”
Exploratory Search: Beyond the Query-Response Paradigm (2009, background): “The reason for narrow suggestions is that most searches on the Web are for known items or are revisitations to previously encountered Web pages (Teevan et al., 2007).”
Understanding User Behavior Through Log Data and Analysis (2014, background): “In this way, researchers have discovered that people often return to common topics when they search (Kotov et al., 2011 ) and even repeat the same query over and over again ( Teevan et al., 2007 ; Tyler & Teevan, 2010 ).”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “[216] Sanderson and Dumais [187] evaluate the re-finding behavior in web search engines.”
Individual Text Corpora Predict User-Specific Knowledge: Benchmarks of Individualized Knowledge Simulation (2026, unknown): “Internet search behavior provides a promising signal: each visited page reflects active engagement with specific content and contributes to a cumulative personal reading history (Marchionini, 2006; Dumais et al., 2003; Teevan et al., 2007).”