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- Publisher
- Emerald Publishing
- Copyright
- © Emerald Publishing Limited
- ISSN
- 1744-0084
- eISSN
- 1744-0084
- DOI
- 10.1108/ijwis-01-2022-0006
- Publisher site
- See Article on Publisher Site
Abstract
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/approachIt 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.FindingsThis 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/valueThe 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.
Journal
International Journal of Web Information Systems – Emerald Publishing
Published: Jul 12, 2023
Keywords: Recommender System; NLP; Collaborative filtering; Content-based filtering