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On a Novel Algorithm for Digital Resource Recommender Systems

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High-Performance Computing and Big Data Analysis (TopHPC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

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Abstract

By increasing the use of users from online resource websites, we need a promising smart recommendation system for high-volume data which has been provided by high-speed service. A lot of research has been done to improve this trend in recent years. Undoubtedly, machine learning has played a main role and has created growing trend in proposer systems evolution. We can point to hybrid recommender systems and content based and collaborative filtering. Different data structures may be used in these methods and we intend to present a new method based on a tree data structure called ckd-tree. We compare this algorithm to different models with different data sets and we got that this algorithm provides a better result for a large system with massive data sets in comparison with other methods. This can be more valuable and also it would be an alternative to common methods like kd-tree.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Department of Computer Science, Kharazmi University, Tehran, Iran

    Keivan Borna & Reza Ghanbari

Authors
  1. Keivan Borna
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  2. Reza Ghanbari
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Correspondence to Keivan Borna .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

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Borna, K., Ghanbari, R. (2019). On a Novel Algorithm for Digital Resource Recommender Systems. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_32

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  • DOI: https://doi.org/10.1007/978-3-030-33495-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33494-9

  • Online ISBN: 978-3-030-33495-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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