Predicting web search hit counts

Document Type

Conference Proceeding

Publication Date

12-13-2010

Abstract

Keyword-based search engines often return an unexpected number of results. Zero hits are naturally undesirable, while too many hits are likely to be overwhelming and of low precision. We present an approach for predicting the number of hits for a given set of query terms. Using word frequencies derived from a large corpus, we construct random samples of combinations of these words as search terms. Then we derive a correlation function between the computed probabilities of search terms and the observed hit counts for them. This regression function is used to predict the hit counts for a user's new searches, with the intention of avoiding information overload. We report the results of experiments with Google, Yahoo! and Bing to validate our methodology. We further investigate the monotonicity of search results for negative search terms by those three search engines. © 2010 IEEE.

Identifier

78649865426 (Scopus)

ISBN

[9780769541914]

Publication Title

Proceedings 2010 IEEE Wic ACM International Conference on Web Intelligence Wi 2010

External Full Text Location

https://doi.org/10.1109/WI-IAT.2010.227

First Page

162

Last Page

166

Volume

1

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