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Recommendation Systems: Algorithms, Challenges,Metrics, and Business Opportunities - 2020

Recommendation Systems: Algorithms, Challenges,Metrics, And Business Opportunities

Research Area:  Machine Learning


Abstract: Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.


Author(s) Name:  Zeshan Fayyaz, Mahsa Ebrahimian, Dina Nawara, Ahmed Ibrahim and Rasha Kashef

Journal name:  Applied Sciences

Conferrence name:  

Publisher name:  MDPI

DOI:  doi:10.3390/app10217748

Volume Information:  2020, 10, 7748