Machine Learning Algorithms for building Recommender Systems
Richa Sharma, Shalli Rani, Sarvesh Tanwar · 2019
Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social- network, health, education, travel, food or tourism. Till date several recommendation approaches have been introduced, the most popular being Content-based filtering, Collaborative filtering, Hybrid and Knowledge based systems. Hybrid systems combine multiple recommendation techniques to enhance the performance of a single recommendation approach and to do so, they follow several hybrid models. This article presents an overview of the state-of-the-art Recommender systems with the prime focus on hybrid recommender systems. Further, different categories of hybridization models are studied, and the existing work is classified categorically based on the hybrid model they follow, and the Machine learning algorithm used.