Feature Engineering based Approach for Prediction of Movie Ratings
Автор: Sathiya Devi S., Parthasarathy G.
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 6 vol.11, 2019 года.
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The buying behavior of the consumer is grown nowadays through recommender systems. Though it recommends, still there are limitations to give a recommendation to the users. In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper. It combines the feature engineering attributes and collaborative filtering for prediction. The proposed system implemented using supervised learning algorithms. The results empirically proved that the mean absolute error of prediction was reduced. This approach shows very promising results.
Recommender systems, gradient boost regression, supervised learning, feature engineering
Короткий адрес: https://sciup.org/15017070
IDR: 15017070 | DOI: 10.5815/ijieeb.2019.06.04
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