Recommendation Analysis on Item-based and User-Based Collaborative Filtering

Providing recommendations to users in every facet of technology is of prior importance for all online and offline web platforms. There are several types of recommender systems, like content-based, context-based, collaborative-based, etc. Collaborative filtering can be classified into User-Based and Item-Based both of these techniques have different advantages and disadvantages. In this paper, we have analyzed the two techniques on a benchmark MovieLens dataset and provided the results stating the performance of each algorithm and analyzing which algorithm provides better results.