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Open AccessArticle

Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System

1
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal
2
Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3597; https://doi.org/10.3390/s20123597
Received: 13 May 2020 / Revised: 23 June 2020 / Accepted: 24 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Sensor-Based, Context-Aware Recommender Systems)
Recommender 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 system. View Full-Text
Keywords: context-aware recommender systems; pre-filtering; fuzzy logic; multi-agent system; multi-armed bandit context-aware recommender systems; pre-filtering; fuzzy logic; multi-agent system; multi-armed bandit
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MDPI and ACS Style

Gomes, L.; Almeida, C.; Vale, Z. Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System. Sensors 2020, 20, 3597.

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