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Can Dissimilar Users Contribute To Accuracy And Diversity Of Personalized Recommendation?

Author

Listed:
  • WEI ZENG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • MING-SHENG SHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • QIAN-MING ZHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • LINYUAN LÜ

    (Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg CH-1700, Switzerland)

  • TAO ZHOU

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
    Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China)

Abstract

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Suggested Citation

  • Wei Zeng & Ming-Sheng Shang & Qian-Ming Zhang & Linyuan Lü & Tao Zhou, 2010. "Can Dissimilar Users Contribute To Accuracy And Diversity Of Personalized Recommendation?," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(10), pages 1217-1227.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:10:n:s0129183110015786
    DOI: 10.1142/S0129183110015786
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    Citations

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    Cited by:

    1. Wei Zeng & An Zeng & Ming-Sheng Shang & Yi-Cheng Zhang, 2013. "Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    2. YingSi Zhao & Bo Shen, 2016. "Empirical Study of User Preferences Based on Rating Data of Movies," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-19, January.
    3. Wei Zeng & An Zeng & Hao Liu & Ming-Sheng Shang & Yi-Cheng Zhang, 2014. "Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-8, October.

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