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Advanced weighted hybridized approach for recommendation system

Debajyoty Banik (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India)
Suresh Chandra Satapathy (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India)
Mansheel Agarwal (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 30 May 2023

Issue publication date: 12 July 2023

113

Abstract

Purpose

This paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application.

Design/methodology/approach

It is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application.

Findings

This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system.

Originality/value

The proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git.

Keywords

Citation

Banik, D., Satapathy, S.C. and Agarwal, M. (2023), "Advanced weighted hybridized approach for recommendation system", International Journal of Web Information Systems, Vol. 19 No. 1, pp. 1-18. https://doi.org/10.1108/IJWIS-01-2022-0006

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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