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Tuning metadata for better movie content-based recommendation systems

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Abstract

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.

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Similar content being viewed by others

Notes

  1. www.netflix.com

  2. www.hulu.com

  3. www.imdb.com

  4. www.amazon.com

  5. www.pandora.com

  6. www.lastfm.com

  7. www.youtube.com

  8. www.ebay.com

  9. www.movielens.umn.edu

  10. http://movielens.umn.edu/

  11. http://developer.netflix.com/

  12. http://www.imdbapi.com/

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Acknowledgments

The work presented in this paper was partially supported by Fundação para a Ciência e Tecnologia, through FCT/UTA Est/MAI/0010/2009 and The Media Arts and Technologies project (MAT), NORTE-07-0124-FEDER-000061, financed by the North Portugal Regional Operational Programme (ON.2-O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT).

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Authors and Affiliations

  1. INESC TEC (formerly INESC Porto), Campus da FEUP, Rua Dr. Roberto Frias, 378, 4200-465, Porto, Portugal

    Márcio Soares & Paula Viana

  2. ISEP/IPP–School of Engineering, Polytechnic Institute of Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Paula Viana

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  1. Márcio Soares
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  2. Paula Viana
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Correspondence to Márcio Soares.

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Soares, M., Viana, P. Tuning metadata for better movie content-based recommendation systems. Multimed Tools Appl 74, 7015–7036 (2015). https://doi.org/10.1007/s11042-014-1950-1

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