A Recent Trends in eBooks Recommender Systems: A Comparative Survey

Abdullah Mohammed Saleh Saleh; Alaa Yaseen Taqa

doi:10.24996/ijs.2024.65.1.39

Authors

Abdullah Mohammed Saleh Saleh Computer Sciences Dept., College of Computer sciences and mathematics, University of Mosul, Nineveh, Iraq https://orcid.org/0009-0006-8610-7045
Alaa Yaseen Taqa Computer Science Dept., College of Education for Pure Sciences, University of Mosul, Nineveh, Iraq https://orcid.org/0000-0002-6345-7708

DOI:

https://doi.org/10.24996/ijs.2024.65.1.39

Keywords:

content-based filtering CBF, Recommender System, Book Recommender system, Recommendation Models, Hybrid system, collaborative filtering, content-based filtering

Abstract

The great progress in information and communication technology has led to a huge increase in data available. Traditional systems can't keep up with this growth and can't handle this huge amount of data. Recommendation systems are one of the most important areas of research right now because they help people make decisions and find what they want among all this data. This study looked at the research trends published in Google Scholar within the period 2018-2022 related to recommending, reviewing, analysing, and comparing ebooks research papers. At first, the research papers were collected and classified based on the recommendation model used, the year of publication, and then they were compared in terms of techniques, datasets utilised, problems, contributions, and evaluation methods used. It was found that many in-depth studies of book recommendation systems directly affect how those systems grow. Many researchers interested in book recommendation systems can learn about the many parts of the field by looking at how the study was put together.