Data Mining Techniques Applied to Recommender Systems for Outdoor Activities: A Systematic Literature Review

Abstract

Currently, many pollutants are released into the air, representing a risk to the environment and human health. There are significant volumes of data generated by the devices that monitor these pollutants. This information can represent a relevant input that allows the construction of applications, techniques, and methodologies to reach a prediction of the state of the air. On the other hand, recommender systems are present in numerous data processing methods, supporting the decision-making and promoting the improvement of the quality of service of solutions. Although several studies have been presented, no secondary studies have been proposed. Therefore, this paper presents a systematic review of the literature, which aims to identify the knowledge areas, tools, methods, and data mining approaches used in recommender systems for outdoor activities related to atmospheric pollutants. The results obtained contribute to creating new ways of recommendation systems based on the previous topics.