Query Log Mining for Inferring User Tasks and Needs

2016 ◽  
pp. 284-288 ◽  
Author(s):  
Rishabh Mehrotra ◽  
Emine Yilmaz
Keyword(s):  
Query Log ◽  
Log Mining ◽  
User Tasks ◽  
Query Log Mining

Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search

2018 ◽  
pp. 266-291
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Multi Agent System ◽  
Information Need ◽  
Agent System ◽  
Query Log ◽  
Information Scent ◽  
Multi Agent ◽  
Log Mining ◽  
Query Log Mining

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.

Privacy-preserving query log mining for business confidentiality protection

2010 ◽  
Vol 4 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Barbara Poblete ◽  
Myra Spiliopoulou ◽  
Ricardo Baeza-Yates
Keyword(s):  
Privacy Preserving ◽  
Query Log ◽  
Log Mining ◽  
Query Log Mining ◽  
Confidentiality Protection

scholarly journals Query-log mining for detecting spam

2008 ◽  
Author(s):  
Carlos Castillo ◽  
Claudio Corsi ◽  
Debora Donato ◽  
Paolo Ferragina ◽  
Aristides Gionis
Keyword(s):  
Query Log ◽  
Log Mining ◽  
Query Log Mining

Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search

2017 ◽  
pp. 131-156
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Multi Agent System ◽  
Information Need ◽  
Agent System ◽  
Query Log ◽  
Information Scent ◽  
Multi Agent ◽  
Log Mining ◽  
Query Log Mining

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.

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