Rating prediction recommendation system based on reviews feature extraction and hidden factor model

Abstract:

Rating prediction is the core issue of the recommendation system research. It predicts the user's rating of the product through the user's historical behavior, and recommends the user's favorite product based on the rating. The current recommendation system based on comment score prediction generally only uses convolutional neural network to capture local features or recurrent neural network to capture global features, ignoring the effective fusion of these two types of features. Aiming at the existing problems, this paper proposes a rating prediction recommendation model based on review feature extraction and hidden factor model, using adaptive receptive field convolutional neural network (CNN) to extract local features, and using gated recurrent unit (GRU) to extract global features. Fusion of different features into embedded representations of reviews. Then combined with the hidden factor model (LFM) to model the user's feature preference and the feature attributes of the product. Finally, the rating prediction is made on the embedded representations of users and commodities. The experimental results show that the model in this paper is higher than the existing baseline model on the five data sets.