Group Typical Preference Extraction Using Collaborative Filtering Profile

  • Su-Jeong Ko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2738)


This paper proposes the association word mining method. Using this method, the profile of the collaborative user is created, and based on this profile, users are grouped according to the vector space model and Kmeans algorithm. Consequently, the existing collaborative filtering system’s problems of sparsity and of recommendations based on the degree of correlation of user preferences are eliminated. Moreover, to address the said system’s shortcoming whereby items are recommended according to the degree of correlation of the two most similar users within a group, entropy is used. Thus, the typical preference of the group is extracted. Since user preferences cannot be automatically regarded as accurate data, users within the group who have entropies beyond the threshold are selected as typical users. The typical preference can be extracted by assigning typical user preferences in the form of weights. The method enables dynamic recommendation because it decreases the inaccuracy of recommendations based on unproven user preferences.


User Preference Collaborative Filter Association Word Vector Space Model Mean Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Su-Jeong Ko
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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