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Applying Random Forest to Drive Recommendation

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Accurate information to users, which is required by online shopping, self-help travel etc. is very important to improve user experience. Recommendation is an important mechanism to match useful information to users with needs. Existing recommendation methods generally rely on massive similarity computation between users and recommended objects, which do not consider some fine-grained information and are not suitable for online recommendation. This paper introduces a novel model for recommendation, which merges classification strategy into recommendation and transforms classification rules into recommendation rules. Random forest is integrated with the proposed model for classification and then a ranking processing is carried out to find top-k users for recommendation. The proposed method makes full use of classification output and the relationships between users and recommended things, it is more suitable for online recommendation. Extensive experiments on different kinds of datasets indicate that the proposed method is effective.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No.61363005, 61462017, U1501252), Guangxi Natural Science Foundation of China(No.2014GXNSFAA118353, 2014GXNSFAA118390), Guangxi Key Laboratory of Automatic Detection Technology and Instrument Foundation(YQ15110), Guangxi Cooperative Innovation Center of Cloud Computing and Big Data.

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Automatic Measurement Technology and Instrument, Guilin University of Electronic Technology, Guilin, 541004, China

    Le Zhan & Qing Yang

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Jingwei Zhang & Yuming Lin

  3. Guangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology, Guilin, 541004, China

    Jingwei Zhang

Authors
  1. Le Zhan
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  2. Jingwei Zhang
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  3. Qing Yang
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  4. Yuming Lin
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Corresponding author

Correspondence to Qing Yang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Zhan, L., Zhang, J., Yang, Q., Lin, Y. (2017). Applying Random Forest to Drive Recommendation. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_51

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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