Movie recommender system with metaheuristic artificial bee

Abstract

Recommender systems are information retrieval tool that allocates accurate recommendations to the specific users. Collaborative movie recommender systems support users in accessing their popular movies by suggesting similar users or movies from their past common ratings. In this research work, a hybrid recommender system has been proposed which utilized k-means clustering algorithm with bio-inspired artificial bee colony (ABC) optimization technique and applied to the Movielens dataset. Our proposed system has been described systematic manner, and the subsequent results have been demonstrated. The proposed system (ABC-KM) is also compared with existing approaches, and the consequences have been examined. Estimation procedures such as precision, mean absolute error, recall, and accuracy for the movie recommender system delivered improved results for ABC-KM collaborative movie recommender system. The experiment outcomes on Movielens dataset established that the projected system provides immense achievement regarding scalability, performance and delivers accurate personalized movie recommendations by reducing cold start problem. As far as our best research knowledge, our proposed recommender system is novel and delivers effective fallouts when compared with already existing systems.