Recommendation System Based on Sparrow Search Optimization Algorithm

The recommendation system is an effective tool used for filtering the huge number of informations and that is highly spread due to the exchanging habits of personalization trends, system users and appearing internet access. Although the current recommender systems are famous for proving the accurate recommendations and it can suffer from several limitations such as sparsity, scalability, cold-start, etc. Because of the various existing techniques, the choice of techniques is a difficult task while creating the applications based on recommender systems. Additionally, every strategy appears with its collection of advantages, limitations and features which increases much more questions, that must be identified. In this paper, the proposed Sparrow Search Optimization Algorithm for recommender system using four various datasets named as MovieLens 100 k, MovieLens 1 million, Jester and Epinion. The performance of the proposed algorithm is estimated by applying parameters like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision and Recall with K-means clustering algorithms. The obtained result shows that this proposed methodology provides high recommendation systems.