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Nome do aluno
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Felipe Rebouças Ferreira Abreu
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Título do trabalho
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An Intelligent Self-Configuring Recommender System as a Service
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Resumo do trabalho
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In today's dynamic digital realm, the plethora of listing services, spanning from music platforms to product recommenders and social media content suggestions, often leaves users searching for items that truly align with their tastes. Addressing this intricacy, the rise of Recommender Systems has proven invaluable. These systems efficiently filter vast data to align items with individual preferences, enhancing user choices. This work centers on the creation of an advanced Recommender Systems API. Distinctively crafted, this API boasts universal accessibility and an uncomplicated deployment procedure. As the foundation for various Web Services, the API draws strength from the stalwart REST architecture. It is designed with a commitment to modularity, championing adaptability and flexibility. The API processes user data and queries to provide tailor-made recommendations quickly. Performance evaluations illuminated the API's commendable accuracy. It particularly shone with smaller datasets, displaying impressive data processing and algorithm execution times. The API manifested exceptional efficiency and resilience under specific test conditions, including cloud environments, especially notable in extensive 16,000-item dataset scenarios. The API is more than a tool; it paves the way for personalized digital experiences, showcasing its prowess in CRUD operations and tailored recommendations. The user evaluation phase encompassed a varied demographic: novice to experienced developers. Over half had considerable software development experience, and a significant percentage had prior engagements with coding recommender systems. With diverse knowledge of recommender libraries, most feedback praised the API's effectiveness. 81% valued the recommendations, and many felt confident in its filtering techniques. The highlight of this work is the Recommender System API's versatility. Despite positive feedback, users suggested improvements in documentation, data security, and features. These insights will shape future API refinements and user experience. Participants' enthusiastic engagement and feedback underscore the API's potential to enhance applications requiring a recommendation system, especially for developers who are perhaps less versed in the theoretical nuances. The solid research foundation and participant dedication highlight the API's potential for broader adoption by developers.
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Orientador
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Frederico Araujo Durão
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Membro Titular Externo (com afiliação)
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Rosalvo Ferreira De Oliveira Neto (UNIVASF)
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Link para o curriculum lattes
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http://lattes.cnpq.br/
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Membro Titular Interno ou Titular Externo 2 (com afiliação)]
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Cláudio Nogueira Sant'Anna (UFBA)
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Link para o curriculum lattes
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http://lattes.cnpq.br/
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Membro Suplente Externo (com afiliação)
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João Batista da Rocha Junior (UEFS)
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Link para o curriculum lattes
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http://lattes.cnpq.br/
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Membro Suplente Interno ou Suplente Externo 2 (com afiliação)
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Maycon Leone Maciel Peixoto (UFBA)
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Link para o curriculum lattes
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http://lattes.cnpq.br/
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Data da defesa
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28 Nov, 2023
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Horário da defesa
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8:30 AM
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