Recommendation Analysis on Item-based and User-Based Collaborative Filtering
Garima Gupta, Rahul Katarya · 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2019
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.