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DCFMRS: Deep Collaborative Filtering for Movie Recommender System

K. Reka, T. N. Ravi


Entertainment industry in this internet era has constantly been taking huge interest in ensuring a tailored experience to each of its audience. Recommender systems are a subclass of information filtering systems and suggesting items especially in streaming services. Streaming services like movie recommendation systems are essential for finding similar users and items. This paper presents deep learning approach based on collaborative filtering that can handle cold start and overfitting problems to provide more reliable predictions. User and item-based collaborative filtering are combined to identify highly items. These items are used to train the deep learning model to predict user ratings on new items and to provide final recommendations. The experimental result of the proposed model has been compared with that of the state of art models in terms of MAE and RMSE.


Recommendation System, Deep Learning, Collaborative Filtering, Multilayer Perceptron, Deep Neural Network.

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