Two-probabilities focused combination in recommender systems

In this paper, we propose a new method called combination for combining information about user preferences on products or services in recommender systems based on Dempster–Shafer theory. Regarding this method, in focal sets of mass functions representing user preferences, focal elements with probabilities in top two highest ones are retained and the remaining focal elements are considered as noise and then transferred to the whole set element. To demonstrate the advantages of the new method, a baseline known as combination is selected for performance comparison in a range of experiments using Movielens and Flixster data sets. According to the results of experiments, the new method is more effective in accuracy of recommendations and comparable in computational time. Also, the new method is capable of overcoming the weakness of the baseline because of the ability to generate stable results.