Paper Info
Title
Characterizing, predicting, and handling web search queries that match very few or no results.
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.
Year
DOI
Venue
2018
10.1002/asi.23955
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
Field
DocType
Volume
Data mining,Range query (database),Web search query,Query language,Search engine,Query expansion,Information retrieval,Computer science,Web query classification,Queries per second,Spatial query
Journal
69.0
Issue
ISSN
Citations 
2.0
2330-1635
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Erdem Sarigil151.09
Ismail Sengor Altingovde232029.96
Roi Blanco387257.42
Berkant Barla Cambazoglu427024.22
Rifat Ozcan519212.83
Özgür Ulusoy61250113.15