Main Article Content
A Recommender System refers to the system which predicts the user preferences in future based on the users rating or review. The automated movie rating prediction helps the user to identify the rating of a movie based on the user preferences. This paper proposes a recommender system which applies an improved TimeFly Algorithm (iTFA) and works based on the changes in user behavior over the time and thus the proposed methodology resolves the problem in fluctuation of users preferences with respect to the time. MovieLens Dataset 100K, 1M, 10M and 20M is used to implement the proposed model. Two similarity measures Cosine similarity measures and Jaccard similarity is used for classification and in order to measure the error rate RMSE and MAE are applied. The results obtained are compared with the classical model and shows that proposed work improves the accuracy in a larger way.