A Review on Methods for Query Personalization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)


Partitioning a set of data (or objects) into a set of meaningful subclasses called clusters is clustering. This paper explains time line of effective algorithms, which personalizes the query using some clustering methods. The methods discussed are as follows: biclique clustering method, concept-based clustering method, personalized concept-based clustering method, content-based query clustering method, k-means clustering method for OLAP queries, personalization based on user preferences, rank-based Web search personalization and agent-based Web search personalization. Personalization has been taken into account in many fields such as data mining, Web search, making the users’ preferences available to them effectively. We can understand how the clustering methods improved toward user personalization. Adding few more attributes such as users, queries, and concepts shall improve personalization of search queries.


Query clustering Biclique Bipartite graph Concept-based clustering Query personalization Content-based clustering OLAP Rank-based personalization Agent-based personalization 


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia

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