Genetic Twin Support Vector Based Movie Recommendation System

Abstract

Movie recommendation systems have become increasingly popular in recent years, as they can help users discover new movies that they are likely to enjoy. However, existing recommendation systems often suffer from limitations such as sparsity, cold start and overfitting. In this paper, we propose a new recommendation system called GATWSVM, which combines genetic algorithm with twin support vector machine to overcome these limitations. GATWSVM works by first constructing a user-movie rating matrix from a set of user-generated ratings. Then, it uses a genetic algorithm to evolve a population of classifiers, each of which is a twin support vector machine. The classifiers are trained to predict the ratings that users would give to movies that they have not yet rated. Finally, the system recommends movies to users by selecting the movies that have the highest predicted ratings.We evaluated GATWSVM on the MovieLens dataset and compared its performance to several state-of-the-art recommendation systems. Our results show that GATWSVM outperforms all of the other systems in terms of recommendation accuracy and precision.