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Application of Recommendation Systems Based on Deep Learning

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1371))

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Abstract

With the rapid development of mobile Internet and self-media, it is becoming more and more convenient for people to obtain information, and the problem of information overload has increasingly affected people’s sense of use. The emergence of recommendation systems can help solve the problem of information overload, but in the big data environment, the traditional recommendation system technology can no longer meet the needs of recommending more personalized, more real-time, and more accurate information to users. In recent years, deep learning has made breakthroughs in the fields of natural language understanding, speech recognition, and image processing. At the same time, deep learning has been integrated into the recommendation system, and satisfactory results have also been achieved. However, how to integrate massive multi-source heterogeneous data and build more accurate user and item models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences between recommendation systems based on deep learning and traditional recommendation systems.

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Authors and Affiliations

  1. Shanghai University of Electric Power, Shanghai, 201306, China

    Li Haiming, Wang Kaili, Sun Yunyun & Mou Xuefeng

Authors
  1. Li Haiming
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  2. Wang Kaili
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  3. Sun Yunyun
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  4. Mou Xuefeng
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Corresponding author

Correspondence to Li Haiming .

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Editors and Affiliations

  1. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Rathachai Chawuthai

  4. Kielce University of Technology, Kielce, Poland

    Pawel Sitek

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Haiming, L., Kaili, W., Yunyun, S., Xuefeng, M. (2021). Application of Recommendation Systems Based on Deep Learning. In: Hong, TP., Wojtkiewicz, K., Chawuthai, R., Sitek, P. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2021. Communications in Computer and Information Science, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-1685-3_8

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  • DOI: https://doi.org/10.1007/978-981-16-1685-3_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1684-6

  • Online ISBN: 978-981-16-1685-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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