Recommender systems: Improving collaborative filtering results

Recommender systems are widely used by companies that sell all or some of their products via the Internet. Furthermore, they are destined to take on an even more important role when their use is generalized as a Web 2.0 social service and is no longer only linked to e-commerce companies. The recommendations that a recommender system offers any given user are based on the preferences shown by a given group of users that have been selected with his/her own similarities. In this paper, we present a series of equations that enable us to obtain each user's importance according to the quality of the recommendations he/she receives and the quality of the recommendations he/she generates. In order to demonstrate the correct operation of the proposed method, we have designed and carried out 90 comparative experiments based on the MovieLens database, whereby we have obtained results that improve the performance of the recommender system at the same time as they increase its levels of accuracy. Each user's values of importance can be used for the following: to restrict or increase the number of recommendations provided to a user, to add information about the reliability of the suggested recommendations, to inform about the level of influence a user has at each time on the recommendations he/she contributes to others, to achieve an objective measurement in order to reward or encourage users with higher levels of importance and even to make it possible to design and implement applications that enable the recommendations made to be monitored and optimized.