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.