Author(s):
1. MOHAMED GRIDA:
Industrial Engineering, Faculty of Engineering, Zagazig University, Egypt
2. LAMIAA FAYED:
Faculty of Computers and Informatics, Zagazig University, Egypt
3. MOHAMED HASSAN:
Information System Department, Faculty of Computers and Informatics, Zagazig University, Egypt
Abstract:
With the increase of information on the internet, more and more electronic data are appearing. Recommender systems were developed to help customers find related items or personalize services. Several online companies apply recommender systems to build up the relationship with users and enhance marketing and sales. Researchers and managers approve that the recommender system offers great opportunities in various domains. Thus, successful development of recommender systems for real-world applications are significant. The most widely used algorithms for recommender systems are categorized into the traditional recommendation and deep-based recommendation algorithms and hybrid recommendation approaches. There is a vital necessity to understand the recommendation system development way. So, this paper presents a structured framework that helps researchers and practical experts recognizing the development phases of the recommender system. The proposed framework is validated through a case study. Furthermore, this paper introduces a general classification scheme for all current recommendation approaches. A summary of historical past recommender system models provided in a way that facilitates understanding their target.
Page(s):
1101-1114
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 7, Year: 2020
Keywords:
Recommender System RS
,
Cold Start
,
Sparsity
,
Deep Learning DL
,
Scalability
,
Structured Framework
References:
References are not available for this document.
Citations
Citations are not available for this document.