Collaborative Filtering in Recommender Systems: Technicalities, Challenges, Applications, and Research Trends
Pradeep Kumar Singh, Pijush Kanti Dutta Pramanik, Prasenjit Choudhury
Abstract
The rapid development and extensive use of recommender systems (RSs) have changed the face of online service experience. The enormous data generated and the complexity involved in analyzing these data for an effective recommendation has attracted researchers from different domains, especially data analytics. In this direction, collaborative filtering (CF) has been the most widely considered approach. The objective of this chapter is to represent a comprehensive study of the CF. The chapter is written in a tutorial fashion so that it can be followed by the readers who are the beginners in this field or unfamiliar with the RS. Different aspects of CF such as classifications, approaches, data extraction methods, similarity metrics, prediction approaches, and performance metrics are studied meticulously. The application of CF in different domains is reviewed. More than 100 research articles are surveyed and categorized according to the application domain of CF they have covered. The challenges involved in the successful adoption of the CF are validly examined. In addition to a brief survey on CF, a systematic survey, considering 277 related papers, on current research trends (2011–2017) on CF is presented. A special discussion of future directions of CF is also stated.