User Demographic Information and Deep Neural Network in Film Recommendation System based on Collaborative Filtering

Abstract

Research about implementation of deep neural network in recommender system based on collaborative filtering received many attentions recently. One of the major problems in deep neural network based collaborative filtering recommendation system was cold-start problem. Some recent work tried to improve model performance by modifying how the model modelled the interaction between user and item features to generate TOP-N recommendations. This work proposed DNCF (Demographic Neural Collaborative Filtering) model that utilized user demographic information and deep neural network architecture to generate film recommendation system based on collaborative filtering in cold-start problem. NCF model was used as baseline model for model performance comparison. Hit Ratio and Normalized Discounted Cumulative Gain for TOP-10 recommendations were used as evaluation metrics for model performance. Experiment results showed that the proposed DNCF model outperform baseline NCF model by 23,61% in HR@10 and 22,40% in NDCG@10 evaluation metrics.