On recommender systems with big data
Lakshmikanth Paleti, Pisipati Radha Krishna, J. V. R. Murthy · 2021
This chapter introduces taxonomy of recommender systems (RSs) in the context of big data and covers the traditional RSs, namely, collaborative filtering (CF)-based and content-based methods along with the state-of-the-art RSs. We also present a detailed study on (a) state of the art methodologies, (b) issues and challenges such as cold start, scalability and sparsity, (c) similarity measures and methodologies, (d) evaluation metrics and (e) popular experimental datasets. This survey explores the breadth in the field of RSs with a focus on big data to the extent possible and tries to summarize them.