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| 2.92 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
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
Description
Keywords
Context-aware recommender systems Pre-filtering Fuzzy logic Multi-agent system Multi-armed bandit
Citation
Publisher
MDPI