A Review on Methods for Query Personalization

TLDR: Time line of effective algorithms, which personalizes the query using some clustering methods, are explained, which can understand how the clusters methods improved toward user personalization.

Abstract: 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.