Data science for next-generation recommender systems

Abstract

Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.

A Review on Job Recommendation System

“…Facebook, Twitter, Netflix, Amazon and LinkedIn are examples of recommendation systems that have gained popularity in recent years [7]. A recommendation system is a combination of data mining, prediction algorithms, machine learning, and other disciplines that help to filter information based on users' preferences and behavior [8]. Personalized recommendation technology has garnered attention from various sectors of society and has been successfully implemented on numerous websites, including news, film, music, and magazines [5,9].…”