A Recommendation Approach Based on Latent Factors Prediction of Recurrent Neural Network

Recommender systems have received much attention due to their wide applications. Current recommender approaches typically recommend items to user based on the rating prediction. However, the predicted ratings cannot truly reflect users interests on items because the rating prediction is usually based on history data and does not consider the effect of time factor on uses interests (behaviors). In this paper, we propose a recommendation approach combining the matrix factorization and a recurrent neural network. In this approach, all the items rated by a user are considered as time series data. The matrix factorization is used to obtain latent vectors of those items. The recurrent neural network is taken as a time series prediction model and trained by the latent vectors of historical items, and then the trained model is used to predict the latent vector of the item to be recommended. Finally, a recommendation list is formed by mapping the latent vector into a set of items. Experimental results show that the proposed approach is able to produce an effective recommend list and outperforms those comparative approaches.