Hourly analysis of a very large topically categorized web query log

By S. Beitzel, Eric C. Jensen, Abdur Chowdhury, D. Grossman, Ophir Frieder. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004

348 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/28e6049b85af685c3ffbb96a0e33165a3b84825f

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In Information Systems

Citations in the corpus

Implicit user modeling for personalized search (2005, background): “There have been many works studying user query logs [1] or query dynamics [13].”
A large-scale evaluation and analysis of personalized search strategies (2007, result): “It also has similar characteristics of those in existing reports [27, 37, 11, 36, 1].”
Understanding User Behavior Through Log Data and Analysis (2014, background): “, 2007], time [Beitzel et al., 2004], and user [Kotov et al.”
What do people ask their social networks, and why?: a survey study of status message q&a behavior (2010, background): “A 2004 study of America Online’s query logs [4] found that the most popular query topics were shopping (13%), entertainment (13%), pornography (10%), computing (9%), health (5%), travel (5%), games (5%), and home (5%).”
The anatomy of a large-scale social search engine (2010, positive): “The distribution is not dissimilar to that found with traditional web search engines [3], but with a much smaller proportion of reference, factual, and navigational queries, and a much greater proportion of experience-oriented, recommendation, local, and advice queries.”
Computing Personalized PageRank Quickly by Exploiting Graph Structures (2014, neutral): “2 on average, see [9].”
Information re-retrieval: repeat queries in Yahoo's logs (2007, background): “Several studies have investigat ed queries in aggregate over time, to understand changes in popul arity [23] and uniqueness of query topics at different times of da y [2].”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “In these papers [111, 231, 209, 156, 158, 35, 211, 109, 147, 162, 126, 110, 112, 34], many interesting statistics are shown.”
Design trade-offs for search engine caching (2008, background): “Beitzel et al. [2004] analyze a very large Web query log containing queries submitted by a population of tens of millions users searching the Web through AOL.”
Modeling and predicting behavioral dynamics on the web (2012, background): “For example, some researchers have investigated aggregations of queries over time to understand changes in the popularity [21] and uniqueness of topics at different times of day [3].”
A picture of search (2006, methodology): “Figures 2.6, 2.7, and 2.8 were taken from [ 4 , 5], with the y-axis given in absolute units; we converted this axis to use a mean difference scale.”