Escaping the echo chamber in movies recommender system

Abstract

With the considerable rise of Internet users and the massive and diverse web searches, an excess of data has become available online. A recommendation model, also known as an engine, handles this massive amount of data available to find patterns that reflect user behaviors. Recommendation models have been implemented in several industries, and the most popular implementation is in the entertainment industry, specifically video streaming and on-demand platforms. There are several types of recommendation systems. In this paper, we have proposed a way to escape the loop created by the recommendation systems, where, the more “same genre” movie we watch, the more of that same genre the recommendation system will propose.