TY - GEN
T1 - Predicting web search hit counts
AU - Tian, Tian
AU - Geller, James
AU - Chun, Soon Ae
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78649865426&partnerID=8YFLogxK
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U2 - 10.1109/WI-IAT.2010.227
DO - 10.1109/WI-IAT.2010.227
M3 - Conference contribution
AN - SCOPUS:78649865426
SN - 9780769541914
T3 - Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
SP - 162
EP - 166
BT - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
T2 - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Y2 - 31 August 2010 through 3 September 2010
ER -