Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/7352
Título: Tuning metadata for better movie content-based recommendation systems
Autor: Soares, Márcio
Viana, Paula
Palavras-chave: Recommendation algorithms
Data: Set-2015
Editora: Springer
Relatório da Série N.º: Multimedia Tools and Applications;Vol. 74, Issue 17
Resumo: The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.
Peer review: yes
URI: http://hdl.handle.net/10400.22/7352
DOI: 10.1007/s11042-014-1950-1
ISSN: 1380-7501
Versão do Editor: http://link.springer.com/article/10.1007%2Fs11042-014-1950-1
Aparece nas colecções:ISEP - DEE - Artigos

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