Modeling prediction in recommender systems using restricted boltzmann machine

Collaborative filtering is a well-known technique used for designing recommender systems when advertising services and products offered to the Internet users. In this paper, we employ the Restricted Boltzmann Machine (RBM) for collaborative filtering and propose the neighborhood-conditional RBM (N-CRBM) model based on joint distributions of similarity and popularity scores. The model is trained and evaluated based on the number of hidden units, learning rates, and activation functions. Simulation results using a dataset consisting of 22 million records show that the proposed N-CRBM model achieves 0.46 average root mean square error (RMSE) and 78.5% accuracy in predicting users' selections of recommended advertisements.