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Exploiting Rating Behaviors for Effective Collaborative Filtering

  • Dingyi Han
  • Yong Yu
  • Gui-Rong Xue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4255)

Abstract

Collaborative Filtering (CF) is important in the e-business era as it can help business companies to predict customer preferences. However, Sparsity is still a major problem preventing it from achieving better effectiveness. Lots of ratings in the training matrix are unknown. Few current CF methods try to fill in those blanks before predicting the ratings of an active user. In this work, we have validated the effectiveness of matrix filling methods for the collaborative filtering. Moreover, we have tried three different matrix filling methods based on the whole training dataset and their clustered subsets with different weights to show the different effects. By comparison, we have analyzed the characteristics of those methods and have found that the mainstream method, Personality diagnosis (PD), can work better with most matrix filling method. Its MAE can reach 0.935 on a 2%-density EachMovie training dataset by item based matrix filling method, which is a 10.1% improvement. Similar improvements can be found both on EachMovie and MovieLens datasets. Our experiments also show that there is no need to do cluster-based matrix filling but the filled values should be assigned with a lower weight during the prediction process.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dingyi Han
    • 1
  • Yong Yu
    • 1
  • Gui-Rong Xue
    • 1
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina

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