Organizing User Search Histories
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Organizing User Search Histories






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Organizing User Search Histories Presentation Transcript

  • 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 2008-11-13 00:01:33 photo example quarter doubled die coin http://www.coi 2008-11-13 00:01:39 plays Perry cox wife scrubs http://www.ref
  • 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.
  • 8. 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.
  • 9. • This module calculates the query relevance by performing random walks over the query fusion graph.
  • 10. 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.
  • 11. 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
  • 12. Thank You