Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007)
0.00
0.0011381751 = product of:
0.0068290504 = sum of:
0.0068290504 = weight(_text_:in in 601) [ClassicSimilarity], result of:
0.0068290504 = score(doc=601,freq=2.0), product of:
0.06491381 = queryWeight, product of:
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.04772181 = queryNorm
0.10520181 = fieldWeight in 601, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.0546875 = fieldNorm(doc=601)
0.16666667 = coord(1/6)
- Abstract
- In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.