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To organize users search history into a set of
The proposed system has the following
•Dynamic Query Grouping
Query Time Query ClickURL
in new or leans
plays Perry cox
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
• 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
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
Dynamic Query Grouping Module
• This module is responsible to group queries
• The proposed similarity function is used to
find the similarity of queries while grouping
• 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