Recommender Systems: Algorithms, Evaluation and Limitations
Mubaraka Sani Ibrahim, Charles Isah Saidu
Journal of Advances in Mathematics and Computer Science · pp. 121–137 · Published 14 May 2020
10.9734/jamcs/2020/v35i230254Abstract
Aims/ objectives: This paper presents the different types of recommender filtering techniques. The main objective of the study is to provide a review of classical methods used in recommender systems such as collaborative filtering, content-based filtering and hybrid filtering, highlighting the main advantages and limitations. This paper also discusses the state-of-art machine learning based recommendation models including Clustering models and Bayesian Classifiers. Further, we discuss the widespread application of recommender systems to a variety of areas such as e-learning and e-news. Finally, the paper evaluates the performance of matrix factorization-based models, nearest neighbours algorithms and co-clustering algorithms in terms of different metrics.
Cited by 5
FengQin ZhuanSun, JiaoJiao Chen, Wenlong Chen · Mathematical Problems in Engineering · 2022
Chenkai Sun, Junxiu An · 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) · 2021
Showing 2 of 5 known citations — external sources report more than can currently be individually listed.
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