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Three-Segment Similarity Measure Model for Collaborative Filtering

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Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

Collaborative filtering is one of the most widely used method in personalized recommendations. The most important step of the method is obtaining the similarities among users by using ratings information so that system can predict user preferences. However, most similarity measures are not efficient enough in the face of cold start and data sparsity problem. To measure user similarity comprehensively and objectively, this paper introduces a segmented similarity measure model. This model not only calculates the similarity model based on the number of user ratings but also makes full use of user attribute similarity and item similarity to improve the accuracy of similarity. Experiments using two reals datasets show that the proposed method relieves cold start and data sparsity issues and improves the prediction accuracy and recommendation quality.

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Acknowledgments

This work was partly supported by the NSFC-Guangdong Joint Found (U1501254) and the Co-construction Program with the Beijing Municipal Commission of Education and the Ministry of Science and Technology of China (2012BAH45B01).

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

    Fangyi Hu

  2. National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China

    Fangyi Hu

Authors
  1. Fangyi Hu
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Corresponding author

Correspondence to Fangyi Hu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Hu, F. (2018). Three-Segment Similarity Measure Model for Collaborative Filtering. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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