Resumen
Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems.