Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations.
Recommendation System is an information filtering system which seeks to predict the “liking” of a user for an item, with the aim to suggest the user those items which he/she is most likely to select/buy. The focus of this paper is on rating prediction whose main objective is to predict the ratings the current user is going to give to the items which are yet to be rated/viewed by him/her. This paper uses a collaborative filtering based approach for generating recommendation, and the model used is a clustering-based model. In this approach all the existing users are clustered using whale optimization technique, instead of traditional clustering approaches like k-means, EM algorithm, etc. The appropriate cluster is then identified for the active user, and the ratings of the active user are predicted based on ratings given by other users belonging to the same cluster. Different measures like MAE, SD, RMSE and t-value are used for performance analysis of the proposed method and the results obtained are found to be highly accurate
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