A Three Level Search Engine Index Based in Query Log Distribution

Author(s):  
Ricardo Baeza-Yates ◽  
Felipe Saint-Jean
Keyword(s):  
Author(s):  
Ji-Rong Wen

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.


Author(s):  
Ji-Rong Wen

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.


1999 ◽  
Vol 33 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Craig Silverstein ◽  
Hannes Marais ◽  
Monika Henzinger ◽  
Michael Moricz

2011 ◽  
Vol 1 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Hamada M. Zahera ◽  
Gamal F. El-Hady ◽  
W. F. Abd El-Wahed

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.


2016 ◽  
Vol 35 (2) ◽  
pp. 1-28 ◽  
Author(s):  
Di Jiang ◽  
Yongxin Tong ◽  
Yuanfeng Song

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