Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

Abstract

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).