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.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

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.

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