Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007)
0.00
0.0029429218 = product of:
0.041200902 = sum of:
0.041200902 = weight(_text_:representation in 601) [ClassicSimilarity], result of:
0.041200902 = score(doc=601,freq=2.0), product of:
0.11578492 = queryWeight, product of:
4.600994 = idf(docFreq=1206, maxDocs=44218)
0.025165197 = queryNorm
0.35583997 = fieldWeight in 601, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.600994 = idf(docFreq=1206, maxDocs=44218)
0.0546875 = fieldNorm(doc=601)
0.071428575 = coord(1/14)
- 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.