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A recommender system for the TV on the web: integrating unrated reviews and movie ratings

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Abstract

The activity of Social-TV viewers has grown considerably in the last few years—viewers are no longer passive elements. The Web has socially empowered the viewers in many new different ways, for example, viewers can now rate TV programs, comment them, and suggest TV shows to friends through Web sites. Some innovations have been exploring these new activities of viewers but we are still far from realizing the full potential of this new setting. For example, social interactions on the Web, such as comments and ratings in online forums, create valuable feedback about the targeted TV entertainment shows. In this paper, we address this last setting: a media recommendation algorithm that suggests recommendations based on users’ ratings and unrated comments. In contrast to similar approaches that are only ratings-based, we propose the inclusion of sentiment knowledge in recommendations. This approach computes new media recommendations by merging media ratings and comments written by users about specific entertainment shows. This contrasts with existing recommendation methods that explore ratings and metadata but do not analyze what users have to say about particular media programs. In this paper, we argue that text comments are excellent indicators of user satisfaction. Sentiment analysis algorithms offer an analysis of the users’ preferences in which the comments may not be associated with an explicit rating. Thus, this analysis will also have an impact on the popularity of a given media show. Thus, the recommendation algorithm—based on matrix factorization by Singular Value Decomposition—will consider both explicit ratings and the output of sentiment analysis algorithms to compute new recommendations. The implemented recommendation framework can be integrated on a Web TV system where users can view and comment entertainment media from a video-on-demand service. The recommendation framework was evaluated on two datasets from IMDb with 53,112 reviews (50 % unrated) and Amazon entertainment media with 698,210 reviews (26 % unrated). Recommendation results with ratings and the inferred preferences—based on the sentiment analysis algorithms—exhibited an improvement over the ratings only based recommendations. This result illustrates the potential of sentiment analysis of user comments in recommendation systems.

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Notes

  1. http://www.amazon.com.

  2. http://www.netflix.com.

  3. https://github.com/JohnLangford/vowpal_wabbit/wiki.

  4. http://sifter.org/~simon/journal/20061211.html.

  5. http://www.imdb.com.

  6. http://www.cs.cornell.edu/people/pabo/movie-review-data.

  7. http://131.193.40.52/data/.

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Acknowledgments

The authors are much appreciated to the authors of [19] who have kindly provided us with their IMDb dataset. This work has been funded by the Portuguese Foundation for Science and Technology under project references UTA-Est/MAI/0010/2009 and PEst-OE/EEI/UI0527/2011, Centro de Informática e Tecnologias da Informação (CITI/FCT/UNL)—2011–2012.

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  1. Departamento de Informática, Centro de Informática e Tecnologias da Informação, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Filipa Peleja, Pedro Dias, Flávio Martins & João Magalhães

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  1. Filipa Peleja
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Correspondence to João Magalhães.

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Peleja, F., Dias, P., Martins, F. et al. A recommender system for the TV on the web: integrating unrated reviews and movie ratings. Multimedia Systems 19, 543–558 (2013). https://doi.org/10.1007/s00530-013-0310-8

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