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A Review on Methods for Query Personalization

  • Shivangi SharmaEmail author
  • Prachi Gupta
Conference paper
  • 733 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

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.

Keywords

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

References

  1. 1.
    Gupta, B., Goyal, S.K., Oberoi, A.: A review on query clustering algorithms for search engine optimization (2012)Google Scholar
  2. 2.
    Foster, I., Kesselman, C.: The grid: blueprint for a new computing infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  3. 3.
    Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Query clustering using content words and user feedback (2001)Google Scholar
  4. 4.
    Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise (1996)Google Scholar
  5. 5.
    Makino, K., Uno, T.: New algorithms for enumerating all maximal cliques. In: The 9th Scandinavian Workshop on Algorithm Theory (2004)Google Scholar
  6. 6.
    Yi, J., Maghoul, F.: Query clustering using click-through graph (2009)Google Scholar
  7. 7.
    Leung, K.W.T., Ng, W., Lee, D.L.: Personalized Concept-based clustering of search engine queries (2008)Google Scholar
  8. 8.
    Koutrika, G.: Query personalization based on user preferencesGoogle Scholar
  9. 9.
    Bentayeb, F., Favre, C.: RoK: Roll-Up with the K-means clustering method for recommending OLAP queriesGoogle Scholar
  10. 10.
    Shen, I.J., Gao, Y., Chen, C., Gong, H.P.: A rank-based prediction algorithm of learning users (2012)Google Scholar
  11. 11.
    Moawad, I.F., Talha, H., Hosny, E., Hashim, M.: Agent-based web search personalization approach using dynamic user profile (2012)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia

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