The DOI matches its Crossref record.
Understanding temporal query dynamics
Abstract
Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent.
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“A more recent study [Kulkarni et al. 2011] explored query intent changes over 10-week time spans and found that 10% of the queries never spiked (atemporal queries), 47% spiked once (temporal unambiguous queries), and 43% spiked multiple times (temporal ambiguous queries).”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kulkarni et al. 2011
In the citing paper
“A more recent study [Kulkarni et al. 2011] explored query intent changes over 10-week time spans and found that 10% of the queries never spiked (atemporal queries), 47% spiked once (temporal unambiguous queries), and 43% spiked multiple times (temporal ambiguous queries).”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kulkarni et al. 2011
In the citing paper
“A more recent study [Kulkarni et al. 2011] explored query intent changes over 10-week time spans and found that 10% of the queries never spiked (atemporal queries), 47% spiked once (temporal unambiguous queries), and 43% spiked multiple times (temporal ambiguous queries).”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Kulkarni et al. 2011
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Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Modeling and predicting behavioral dynamics on the web
Radinsky, K., Svore, K., Dumais, S., Teevan, J., Bocharov, A., & Horvitz, E. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187918 · Cited by 122
Self-citation
Metadata
- Authors
- Kulkarni, A., Teevan, J., Svore, K. M., & Dumais, S. T.
- Affiliations
- Carnegie Mellon University, Pittsburgh, PA, USA
- Microsoft Research, Redmond, WA, USA
- Venue
- Proceedings of the fourth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2011-02-09
- Pages
- 167-176
- Cited by
- 94
- References
- 0
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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