A Comparative Study of Recommender Systems

Abstract

The advent of modernization has led to a spurt in technological advancements. Consequently, the internet boom has resulted in the way we live our lives. The Internet has served as a platform for businesses which has yielded better results as compared to the traditional way of outdoor selling. Amazon, Netflix, Flipkart are such companies that have flourished online. These businesses use recommender systems to increase their selling based on customer preferences. These systems not only use a customer’s buying habits but also involve knowledge of reviews, ratings, correlation, similarity etc. involving several customers and items. Recommendation systems have been a part of research in their own respect. New robust algorithms have been developed over time aimed at improving system efficiency. This paper highlights the types of recommendation systems. Different systems follow different principles for providing user recommendations. An overview of such systems is showcased in the paper. The paper proposes a model based on a hybrid recommendation technique involving a combination of content and collaborative approaches.