A Machine Learning Approach for Item Recommendation Using SVD Technique
Vishal Paranjape, Neelu Nihalani, Nishchol Mishra · 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) · 2021
Today recommender system plays a very prominent role in suggesting and offering products to their customers on the basis of their interests and choices. We encounter these systems in our day to day life while interacting with several online based services. There are several models employed in various e-commerce based systems like amazon, flipkart etc. Our proposed architecture in this paper deploys a machine learning approach for item recommendation making use of collaborative based model. We are following the model based approach using the concept of singular value decomposition (SVD) for getting the ratings of unrated items and minimizing the sparsity problem. Our work also focus on calculating the Root Mean Square Error (RMSE) of our proposed system to check it's accuracy.