I. Introduction
With the use of recommendation methods, recommender systems [1], [2], which are web-based support systems, actively suggest a set of limited and ranked items from all available items without the direct input of users. These systems are widely used to overcome the problems created by the so-called “information explosion” in a variety of web-based applications in e-commerce [3], e-learning [4], and e-tourism, as well as in such areas as the recommendation of news, movies, books, videos, resources [5], and real estate [6]. Prior to making a recommendation, recommender systems use background data, such as historical data consisting of ratings from users, and input data, such as features of items or user ratings, to initiate a recommendation; models and algorithms combine the two and generate a recommendation [7], [8].