Characterizing, predicting, and handling web search queries that match very few or no results
Author
Sarigil, E.
Altingovde, I. S.
Blanco, R.
Cambazoglu, B. B.
Ozcan, R.
Ulusoy, Özgür
Date
2018Source Title
Journal of the Association for Information Science and Technology
Print ISSN
2330-1635
Electronic ISSN
2330-1643
Publisher
John Wiley & Sons
Volume
69
Issue
2
Pages
256 - 270
Language
English
Type
ArticleItem Usage Stats
148
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views
141
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downloads
Abstract
A non‐negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no‐answer queries, we devised a large number of features that can be used to identify such queries and built machine‐learning models. These models can be useful for scenarios such as the mobile‐ or meta‐search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.