Restricted Boltzmann Machine Based on Item Category for Collaborative Filtering
Fan He, Na Li · 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC) · 2017
To improve the accuracy of recommendation, an new model is proposed in this paper, which solves the problem of data sparseness of collaborative filtering to a certain extent. On the base of Real_value Restricted Boltzmann Machine (R_RBM), the impact of item category on recommendation results are considered. The item category, as a new layer, is added to R_RBM, which aims to predict the missing rating in the matrix. Then the recommendation is produced by combining ICR_RBM and collaborative filtering. The experimental results on MovieLens show that recommended effect of new model is more efficient than traditional RBM and singular value decomposition.