Distributed deep Autoencoder for recommendation system
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Recommender systems are one of the prominent area often the researchers are attracting to apply the concepts of Deep neural networks. Many of them introduced the problems facing by users like information load, Sparsity, Top-K recommendations, rating predictions and user feedback and giving best suitable solutions. Many machine learning and matrix factorization techniques are used to solve the problems but problems are linear modeling. Now in this paper, we propose distributed auto encoder with optimization to capture non-linear relationships between users and items to give predictions for missing values, giving top-k recommendations. We did several experiments on movielens 100k,movielens20M as compared with base line methods. Our proposed model performance outperforms the other existing models in terms of RMSE, MAE, HitRatio and Novelty evaluation metrics.