A Hybrid Deep Learning Method to Extract Multi-features from Reviews and User–Item Relations for Rating Prediction

Abstract

Currently, the Internet is widely used for shopping. Online reviews have become a crucial factor in helping people to make purchasing decisions. However, the large amount of data overwhelms most users, leading to the problem of information overload. To address this issue, researchers have proposed recommender systems as a solution. The most commonly used method is the collaborative filtering method, which analyzes users’ purchase history or behavior to make recommendations. In addition to user ratings, by analyzing users’ comments and the relationships between users and items more precise preferences can be obtained. In this study, the aspect-based rating prediction with a hybrid deep learning method (ARPH) is proposed. It consists of five parts: aspect detection, sentiment and semantic analysis, user preference analysis, graph attention network analysis, and rating prediction. It initially extracts the implicit aspect features and aspects’ sentiment–semantic features from user and item reviews. The convolutional neural network and matrix factorization methods are then used to generate the predicted ratings of items. Additionally, a graph attention network was built to calculate the predicted ratings based on the relationships between users and items. Finally, a multilayer perceptron was used to automatically adjust the weights for integrating these two predicted ratings. Our method utilizes user–item relationships to predict ratings when there are fewer user reviews. Conversely, the features derived from textual reviews were employed for rating prediction. The experimental results showed that extracting different features is useful in representing user and product preferences. The proposed method effectively improved the accuracy of the rating predictions.