Abstract
This paper extends a previous work done by the same authors [1] having the aim of improving the predictions coming from a matrix factorization based on latent factor models through an ensemble with the predictions obtained by an Opinion Mining methodology based on a linguistic approach. The experimental analysis was carried out on the Yelp business dataset, limited to the Restaurant category. An hypothesis of influence of the restaurant average rating on the number of stars given by the users is tested. An analysis of the meaning of some of the latent factors is shown.
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Acknowledgements
This study is part of a POR FESR 2007-2013 project co-funded by the Autonomous Region of Sardinia: Comunimatica (P.I.A. n. 205 co-funded according to the DGR 39/3 of 10/11/2012).
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Angioni, M., Clemente, M.L., Tuveri, F. (2016). Improving Predictions with an Ensemble of Linguistic Approach and Matrix Factorization. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_9
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DOI: https://doi.org/10.1007/978-3-319-30996-5_9
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