ABSTRACT
Modern recommendation systems require access to and understanding of the big data built on top of large data islands. This is important as the growing enhancement in interconnection, storage, as well as data management has made it possible to connect to a data deluge from big data—which in turn, can lead to making intelligent and accurate personalization and recommendations. Despite the effectiveness of the presented methods, in the times of big data and AI, the need for more advanced methods has been a strong force to build intelligent systems for quick, accurate, and personalized recommendations tailored to each customer’s needs and preferences. In this chapter, we provide an overview of different types of real-world recommender systems, along with challenges and opportunities in the age of big data and AI. We will discuss how recent growth in cognitive technology, together with advancement in areas such as AI (plus ML, DL and NLP) as well as knowledge representation, interaction, and personalization, have resulted in the substantial enhancement in the research of recommender systems.
