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User Query Clustering for Web
Personalization
Contents
• Objective
• Modules
• Input Dataset
• Module Description
• References
• Objective:
To organize users search history into a set of
query groups.
Modules
The proposed system has the following
modules:
•Query Group
•Search History
•Query Relevance
•Dynamic Query Grouping
Input Dataset
Query Time Query ClickURL
2008-11-13
00:01:30
kitchen counter
in new or leans
http://www.su
perpages.com
2008-11-13
00:01:33
photo example
quarter
doubled die
coin
http://www.coi
nresource.com
2008-11-13
00:01:39
plays Perry cox
wife scrubs
http://www.ref
erence.com
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.
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.
Organizing User Search Histories
Organizing User Search Histories
Organizing User Search Histories
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.
• This module calculates the query relevance by
performing random walks over the query
fusion graph.
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.
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
Thank You

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Organizing User Search Histories

  • 1. User Query Clustering for Web Personalization
  • 2. Contents • Objective • Modules • Input Dataset • Module Description • References
  • 3. • Objective: To organize users search history into a set of query groups.
  • 4. Modules The proposed system has the following modules: •Query Group •Search History •Query Relevance •Dynamic Query Grouping
  • 5. Input Dataset Query Time Query ClickURL 2008-11-13 00:01:30 kitchen counter in new or leans http://www.su perpages.com 2008-11-13 00:01:33 photo example quarter doubled die coin http://www.coi nresource.com 2008-11-13 00:01:39 plays Perry cox wife scrubs http://www.ref erence.com
  • 6. 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.
  • 7. 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.
  • 11. 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.
  • 12. • This module calculates the query relevance by performing random walks over the query fusion graph.
  • 13. 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.
  • 14. 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