INFORMATION FILTERING BASED ON USERS' NEGATIVE OPINIONS

Qiang Guo, Yang Li and Jian-Guo Liu ()
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Qiang Guo: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Yang Li: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Jian-Guo Liu: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China

International Journal of Modern Physics C (IJMPC), 2013, vol. 24, issue 05, 1-15

Abstract: The process of heat conduction (HC) has recently found application in the information filtering [Zhanget al.,Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

Keywords: Information filtering; heat conduction; user-object bipartite networks; negative opinion; 89.75.Hc; 87.23.Ge; 05.70.Ln (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183113500320

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