organizing user search histories
TRANSCRIPT
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User Query Clustering for Web Personalization
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Contents
• Objective• Modules• Input Dataset• Module Description• References
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• Objective: To organize users search history into a set of
query groups.
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Modules
The proposed system has the following modules:•Query Group•Search History•Query Relevance•Dynamic Query Grouping
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Input Dataset
Query Time Query ClickURL
2008-11-13 00:01:30
kitchen counter in new or leans
http://www.superpages.com
2008-11-13 00:01:33
photo example quarter doubled die coin
http://www.coinresource.com
2008-11-13 00:01:39
plays Perry cox wife scrubs
http://www.reference.com
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Query Group Module
• This module is responsible for computing groups. • First and foremost, query grouping allows the
search engine to better understand a user’s session and potentially tailor that user’s search experience according to her needs.
• Once query groups have been identified, search engines can have a good representation of the search context behind the current query using queries and clicks in the corresponding query group.
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Search History
• This module is responsible for storing the search history of the user.
• User’s search history consists of the Query, URL with the corresponding time and date.
• User’s search history is stored in the database which is used for organizing according to the group.
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Query Relevance Module
• This module is responsible to compute query relevance between two queries using QFG.
• The edges in Query Fusion Graph correspond to pairs of relevant queries extracted from the query logs and the click logs.
• Query Fusion Graph merges the information of both Query Reformulation Graph and Query Click Graph.
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• This module calculates the query relevance by performing random walks over the query fusion graph.
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Dynamic Query Grouping Module
• This module is responsible to group queries dynamically.
• The proposed similarity function is used to find the similarity of queries while grouping them.
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References• Organizing User Search Histories Heasoo Hwang, Hady W. Lauw, Lise
Getoor, and Alexandros Ntoulas IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 5, MAY 2012
• Agglomerative clustering of a search engine query log Doug Beeferman Lycos Inc. 4002 Totten Pond Road Waltham, MA 02451
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Thank You