A Comparison of BAT and Firefly Algorithm in Neighborhood based Collaborative Filtering

The recommender system is a knowledge-based filtering system that predicts the users' rating and preference for what they might desire. Simultaneously, the neighborhood method is a promising approach to perform predictions, resulting in a high accuracy based on the common items. This method, furthermore, could affect the resulting accuracy value because when each user provides limited data and sparsity, the accuracy of value might be narrow down as a consequence. In this research, we use the Swarm Intelligent (SI) technique in the recommender system to overcome this problem, whereby SI will train each feature to optimal weight. This technique's main objective is to form better groups of similar users and improve recommendations' accuracy. The intelligent swarm technique used to compare its accuracy to help provide recommendations is the Firefly and Bat Algorithm. The results show that the Firefly Algorithm has slightly better performance than the Bat Algorithm, with a difference in the mean absolute error of 0.02013333. The significance test using the independent t-test method states that no statistically significant difference between Bat and Firefly algorithm.