Gomes, LuisAlmeida, CarlosVale, Zita2021-09-202021-09-202020-06http://hdl.handle.net/10400.22/18445Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed systemengContext-aware recommender systemsPre-filteringFuzzy logicMulti-agent systemMulti-armed banditjournal article10.3390/s20123597