{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T12:50:51Z","timestamp":1769431851302,"version":"3.49.0"},"reference-count":67,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.<\/jats:p>","DOI":"10.3390\/electronics11213466","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"3466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Context-Based Multi-Agent Recommender System, Supported on IoT, for Guiding the Occupants of a Building in Case of a Fire"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1228-1236","authenticated-orcid":false,"given":"Joaquim","family":"Neto","sequence":"first","affiliation":[{"name":"LNEC\u2014Laborat\u00f3rio Nacional de Engenharia Civil, 1700-066 Lisboa, Portugal"},{"name":"Universidade Aberta, 1269-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2224-1609","authenticated-orcid":false,"given":"Ant\u00f3nio Jorge","family":"Morais","sequence":"additional","affiliation":[{"name":"Universidade Aberta, 1269-001 Lisboa, Portugal"},{"name":"LIAAD\u2014INESC TEC, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8698-866X","authenticated-orcid":false,"given":"Ramiro","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"UTAD\u2014Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"given":"Ant\u00f3nio Le\u00e7a","family":"Coelho","sequence":"additional","affiliation":[{"name":"Universidade Lus\u00f3fona do Porto, 4000-098 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","first-page":"abs\/1303.3","article-title":"Towards a serious games evacuation simulator","volume":"2013","author":"Ribeiro","year":"2013","journal-title":"CoRR"},{"key":"ref_2","unstructured":"Cordeiro, E. 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