Abstract:
The recommendation system based on deep learning is the trend of current development. In order to solve the problem about the prediction accuracy decreases due to the sparse data, in this paper, a deep collaborative filtering recommendation system based on comment information is proposed. Firstly, the user’s or item’s comment text is embedded and represented, and it is sent to BiGRU layer to enhance the relevance of the words before and after the long text. Then, a double-layer attention mechanism is used to allocate the weight of different comments’ contribution to the model. Finally, PMF is used to fuse the user model and the item model to predict the user’s rating on the item. The experimental results show that this model can significantly reduce the error of score prediction, so as to effectively improve the accuracy of recommendation.