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Personalized Recommendation Algorithm Based on Commodity Label

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 873))

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Abstract

Aiming at the fact that the traditional collaborative filtering recommendation algorithm is insufficient in the number of users’ implicit feedback, and the user interest preference model is too rough, a collaborative filtering recommendation algorithm with the importance of tags is proposed. Type and frequency of use of the label reflect user preferences and preferences, in order to establish a new user preferences model for better mining and use implicit user feedback data will affect the degree of the label on the user to quantify, to establish a new method for similarity computation. The experimental results show that the proposed algorithm has obvious advantages, improves the recommendation accuracy and alleviates the cold start problem.

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Authors and Affiliations

  1. Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Yuehua Dong & Xuelei Liang

Authors
  1. Yuehua Dong
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  2. Xuelei Liang
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Corresponding author

Correspondence to Xuelei Liang .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Dong, Y., Liang, X. (2018). Personalized Recommendation Algorithm Based on Commodity Label. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_43

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  • DOI: https://doi.org/10.1007/978-981-13-1648-7_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1647-0

  • Online ISBN: 978-981-13-1648-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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