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A Survey of the State-of-the-Art and Some Extensions of Recommender System Based on Big Data

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Big Data and Security (ICBDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1796))

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Abstract

Recommender systems (RSs) based on big data have been shown to be very powerful tools for solving the information overload to assist the choice-making when dealing with the massive amount information in the age of big data and artificial intelligence. This paper presents an overview of the state-of-art RS that can be classified into four categories: content-based algorithms (CR), collaborative filtering-based algorithms (CF), and knowledge-based algorithms (KR), as well as hybrid recommendation-based algorithms (HR). The popular CF-based recommender algorithms are especially focused by classifying them into the memory-based algorithms, and model-based algorithms as they show the advantages of great rating prediction without contextual features compared to the rest of RS approaches. By reviewing the current RS and understanding their limitations, the emerging solutions or possible extensions that would improve recommendation capabilities involving deep learning, knowledge graphs, and parallel computing techniques are significantly discussed for future RS research direction. At the same time, by identifying current problems, some possible solutions will be shown in the last part.

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Acknowledgement

This work is supported by the grant of National Natural Science Foundation of China (62103184), in part by China Postdoctoral Science Foundation (2021M690630), in part by Basic Science (Natural science) Research project of Jiangsu Province(No.22KJB510022), in part by Jiangsu Provincial Double-Innovation Doctor Program (No. (2020)30696), in part by Scientific Research Foundation of Nanjing Institute of Technology of China (No.YKJ201978).

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Authors and Affiliations

  1. Nanjing Tech University Nanjing, Nanjing, China

    Lixin Jia & Lihang Feng

  2. Nanjing Institute of Technology, Nanjing, China

    Lixiu Jia

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  1. Lixin Jia
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  2. Lixiu Jia
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  3. Lihang Feng
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Corresponding author

Correspondence to Lihang Feng .

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Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Tinghuai Ma

  3. Chinese Academy of Sciences, Shenzhen, China

    Qingshan Jiang

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Qi Liu

  5. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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Jia, L., Jia, L., Feng, L. (2023). A Survey of the State-of-the-Art and Some Extensions of Recommender System Based on Big Data. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_12

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  • DOI: https://doi.org/10.1007/978-981-99-3300-6_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3299-3

  • Online ISBN: 978-981-99-3300-6

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