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TRABALHOS DE FIM DE CURSO @PUC-Rio
Consulta aos Conteúdos
Estatística
Título: COMPARATIVE STUDY OF MOVIE RECOMMENDATION ALGORITHMS
Autor(es): PEDRO CHAMBERLAIN MATOS
Colaborador(es): MARCO SERPA MOLINARO - Orientador
Catalogação: 03/MAR/2022 Língua(s): PORTUGUESE - BRAZIL
Tipo: TEXT Subtipo: SENIOR PROJECT
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=57546@1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=57546@2
DOI: https://doi.org/10.17771/PUCRio.acad.57546
Resumo:
Personalized recommendations for series and movies are an important aspect of online streaming services. This project s objective was to implement and evaluate a series of algorithms used by movie recommendation systems. Four recommendation algorithms were analyzed, two by method of collaborative filtering and two by method of content-based filtering. In addition to the analysis of these, a new hybrid method was developed using two of the priorly analyzed algorithms: a collaborative filtering algorithm based on matrix factorization by singular value decomposition (SVD) and a content-based filtering algorithm using a similarity calculation of textual information between movies. The new method was implemented and analyzed, exhibiting better results than all previous methods. The evaluation data was based on movie ratings given by users of the MovieLens social platform.
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