Recommender System: A bibliometric analysis. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Sharma, Sheetal; Gupta, Kamali; Gupta, Deepali
• Is Part Of: IOP conference series. Volume 1022:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Recommender system -- Collaborative Filtering -- Content-based filtering -- Hybrid filtering -- Analysis; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1022/1/012057
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The exponential growth in the online share of businesses has to lead to a gigantic wave of options available to the active user. Recommender systems, therefore assist the users to go through the tailored list of products to match their preferences. A range of recommender systems is available to serve the purpose. This article will navigate through the basic of recommender systems, and its classifications types viz. collaborative filtering, content-based filtering, demographic, hybrid, and knowledge-based recommender system. It aims to analyze publications of the Scopus database using biblioshiny tool of RStudio software. A bibliometric analysis is conducted on 556 papers to analyze the recent research trends in recommendation systems. Further, challenges have also been discussed that need to be dealt with the recommender system.