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Relevance and Time Based Collaborative Filtering for Recommendation

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12239))

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Abstract

In our daily life, many recommendation systems are designed based on collaborative filtering algorithm. For example, video recommendations, shopping recommendations, music recommendations, and so on. However, the traditional collaborative filtering algorithm has problems such as low recommendation accuracy and poor real-time performance. In this paper, an enhanced collaborative filtering algorithm considering the relevance factor and time factor is proposed to improve the accuracy and timeliness of recommendations. Meantime, the average absolute error is utilized as an indicator to measure the recommended effect. Simulations are given to prove the effeteness of the proposed algorithm.

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Funding

This research was funded by National Key R&D Program of China (No. 2018YFB1003905).

Author information

Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, 100083, China

    Aobo Zhang & Haitao Xu

  2. China Telecommunication Technology Labs-Terminals, China Academy of Information and Communications Technology, Beijing, 10083, China

    Ruijie Lin

Authors
  1. Aobo Zhang
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  2. Ruijie Lin
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  3. Haitao Xu
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Corresponding author

Correspondence to Haitao Xu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zhang, A., Lin, R., Xu, H. (2020). Relevance and Time Based Collaborative Filtering for Recommendation. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_49

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  • DOI: https://doi.org/10.1007/978-3-030-57884-8_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57883-1

  • Online ISBN: 978-3-030-57884-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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