Algoritmos de Recomendação: precepção dos estudantes de Administração de Uberlândia, MG

Abstract

Since the emergence of the internet, social networks have revolutionized communication, becoming essential for human interaction as well as for promoting brands. They enable the creation of emotional bonds with consumers and boost sales, functioning as direct advertising tools. Furthermore, recommendation algorithms, which monitor user interactions, play a decisive role in personalizing social media experiences. These algorithms, by identifying behavior patterns, suggest content that may interest users, favoring product acceptance and increasing brand visibility. The research carried out has a quantitative approach and focuses on Administration students from Uberlândia, MG, to assess their level of understanding of recommendation systems on social networks. Analysis of results revealed that Instagram is the most popular social network in this academic group, followed by TikTok and LinkedIn. The majority use social networks 2 to 4 hours a day, with men tending to spend more time online. Regarding artificial intelligence (AI), 52% have already taken classes on the topic, with greater interest among men. When interacting with ads, posts that offer discounts attract users more, while questionnaires have less appeal. As for recommendation systems, the excess of repetitive suggestions and the difficulty in finding new products are highlighted as main annoyances. Users want more diversity and relevance in suggestions, for a more satisfying experience.