Abstract
In this paper, we describe a novel Gaussian process model for TV audience rating prediction. A weight-sharing covariance function well-suited for this problem is introduced. We extract several types of features from Google Trends and Facebook, and demonstrate that they can be useful in predicting the TV audience ratings. Experiments on a dataset consisting of daily dramas show that the proposed model outperforms the other conventional models given the same feature set.
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Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 01, pp. 492–499. IEEE Computer Society, Washington, DC (2010), http://dx.doi.org/10.1109/WI-IAT.2010.63
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3), 307–327 (1986)
Cawley, G.C., Talbot, N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research 11, 2079–2107 (2010), http://dl.acm.org/citation.cfm?id=1756006.1859921
Cheng, Y.H., Wu, C.M., Ku, T., Chen, G.D.: A predicting model of tv audience rating based on the facebook. In: International Conference on Social Computing (SocialCom), pp. 1034–1037. IEEE (2013)
Danaher, P., Dagger, T.: Using a nested logit model to forecast television ratings. International Journal of Forecasting 28(3), 607–622 (2012)
Danaher, P.J., Dagger, T.S., Smith, M.S.: Forecasting television ratings. International Journal of Forecasting 27(4), 1215–1240 (2011)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008), http://dl.acm.org/citation.cfm?id=1390681.1442794
Hsieh, W.T., Chou, S.C.T., Cheng, Y.H., Wu, C.M.: Predicting tv audience rating with social media. In: Proceedings of the IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 1–5. Asian Federation of Natural Language Processing, Nagoya (2013), http://www.aclweb.org/anthology/W13-4201
Luxhøj, J.T., Riis, J.O., Stensballe, B.: A hybrid econometric-neural network modeling approach for sales forecasting. International Journal of Production Economics 43(2), 175–192 (1996)
Murray, I., Adams, R.P.: Slice sampling covariance hyperparameters of latent gaussian models. In: Advances in Neural Information Processing Systems, pp. 1723–1731 (2010)
Neal, R.M.: Bayesian Learning for Neural Networks. Springer-Verlag New York, Inc., Secaucus (1996)
Posedel, P.: Analysis of the exchange rate and pricing foreign currency options on the croatian market: the ngarch model as an alternative to the black-scholes model. Financial Theory and Practice 30(4), 347–368 (2006)
Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
Schwaighofer, A., Tresp, V.: Transductive and inductive methods for approximate gaussian process regression. In: Advances in Neural Information Processing Systems (2003)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer (2000)
Williams, C.K.I., Rasmussen, C.E.: Gaussian processes for regression. In: Advances in Neural Information Processing Systems, pp. 514–520. MIT Press (1996)
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Huang, YY., Yen, YA., Ku, TW., Lin, SD., Hsieh, WT., Ku, T. (2014). A Weight-Sharing Gaussian Process Model Using Web-Based Information for Audience Rating Prediction. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_19
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DOI: https://doi.org/10.1007/978-3-319-13987-6_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
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