Research on Mahout-based personalised recommendation application for libraries

Authors

  • Yanpeng Tu

DOI:

https://doi.org/10.54691/bcpbm.v29i.2322

Keywords:

Collaborative filtering, Similarity, Recommender systems, Hadoop, Mahout

Abstract

Personalised recommendation systems are widely used in various fields because they can provide personalised recommendation services to users based on their characteristics or historical behaviour data. This paper addresses the problem of low utilization of library resources due to the lack of personalized service capability in libraries, and implements personalized recommendation service in libraries through the collaborative filtering recommendation module provided by Mahout framework, and the test results also verify the initial personalized recommendation effect.

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References

OU Wei-hong, YANG Yong-qin. Research on the Book Recommendation System Based on Mahout under the Big Data Platform [J], (Guangzhou University of Science and Technology, Guangzhou 510550, Guangdong).

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Wu Jieming, Li Wenxi, Research and design of a Mahout-based book recommendation engine [J], Industrial technology innovation. 2015, 2(03): 342-348.

Li Hairong, Fang Zhongchun. Design and implementation of a recommendation system based on Mahout and collaborative filtering algorithms [J], Journal of Inner Mongolia University of Science and Technology. 2018, 37(03):260-263.

Zhu Lijun. Design and implementation of a personalized book recommendation system for university libraries based on mahout [D], Nanchang University, 2018.

Zhang Yue. Design and implementation of a Mahout-based book recommendation system [D], Shanxi University, 2017.

Yan Liyang. Design and implementation of Mahout-based personalized book recommendation system [D], Northwest University for Nationalities, 2019.

Chang Jiang. Research and implementation of an Apache Mahout-based recommendation algorithm [D]. University of Electronic Science and Technology, 2013.

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Published

2022-10-12

How to Cite

Tu, Y. (2022). Research on Mahout-based personalised recommendation application for libraries. BCP Business & Management, 29, 550-555. https://doi.org/10.54691/bcpbm.v29i.2322