Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user interests. In this paper, to achieve the mentioned aim we use a combination of K-means and differential evolution algorithms. The K-means algorithm determines the best recommendations for the current user based on the behavior of the other users. The differential evolution algorithm is used to optimize the user clustering in the recommender system. Given that the proposed model has been tested in a movie domain, the films suggested to the current user, have the highest rates from the users who are similar to the current user. The results gained from the simulation show the superior performance of the proposed model in comparison to the related works with an average increased accuracy of 0.01.

Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms Keywords:

Recommender Systems , K-means , differential evolution algorithms , Clustering , Accuracy