A collaborative filtering recommender system with randomized learning rate and regularized parameter

Recommender systems with the approach of collaborative filtering by using the algorithms of machine learning gives better optimized results. But selecting the appropriate learning rate and regularized parameter is not an easy task. RMSE changes from one set of these values to others. The best set of these parameters has to be selected so that the RMSE must be optimized. In this paper we proposed a method to resolve this problem. Our proposed system selects appropriate learning rate and regularized parameter for given data.