RecomendaAí: do pipeline de dados à recomendação personalizada em aplicação web

Abstract

The RecomendaAí is a web application for movie and TV series recommendation developed in the context of Applied Data Science, focusing on mitigating choice overload in extensive audiovisual catalogs. The solution uses real data from The Movie Database (TMDb) API, consolidated into a processed catalog of 18,128 titles, to generate recommendations within

an integrated environment that includes catalog exploration, authentication, personaliza- tion, and user interaction tracking. The methodology combines content-based filtering

through TF-IDF (Term Frequency–Inverse Document Frequency) vectorization and cosine similarity, with optional semantic search via embeddings and hybrid reranking strategies

based on behavioral signals and usage context. The application organizes the user experi- ence into the Search, Discover, and For You tabs, enabling recommendations from known

titles, natural-language queries, and continuous personalization from interaction history. This work demonstrates the integration of Data Science techniques and web development in building a functional, interpretable, and usable recommendation system capable of reducing the user’s search space in large catalogs.