Predicting Users’ Preferences for Movie Recommender System Using Restricted Boltzmann Machine

Recommender system is one of the most important crucial parts for e-commerce domains, enabling them to produce correct recommendations to individual users. Collaborative filtering is considered as the successful technique for recommender system that takes rating scores to find most similar users/items for recommending items. In this work, in order to exploit user rating information, a model has been developed that uses Restricted Boltzmann Machine (RBM) to learn deeply and predict the ratings or preferences which are missed. The experiment is done on MovieLens benchmark dataset that compares with Pearson correlation and average prediction-type algorithms. Experimental result exhibits the performance of RBM to predict users’ preferences.

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