## 3 The Proposed Method  
### Research Overview  
This study proposes an aspect-based deep learning rating prediction method (ADLRP) for review sites. This method is used to analyze the aspects of user reviews and their preferences for different aspects, as well as the product reviews according to different aspects. The user and product features are then integrated to predict future products of interest for users. Previous studies have usually only analyzed the historic rating values for predictions and recommendations, but ignored users' preferences and product features implied in the text reviews. In addition, different aspects, sentiment, and semantics hidden in the text reviews can express user preferences or product evaluation. If we only analyze user and product ratings, we cannot thoroughly understand the user's preferences and needs. Therefore, considering user-preference aspects, semantic features, and sentiment features hidden in text reviews helps to gain insight into user preferences and product features. Combining the implicit (i.e., the aspect, semantic, and sentiment features) and explicit information (i.e., user ratings) in a rating prediction method can accurately and effectively recommend products that may be of interest to users.  
Figure 1 shows the research process of the proposed method. This study takes the Electronics category of Amazon Datasets ([[https://jmcauley.ucsd.edu/data/amazon/](https://jmcauley.ucsd.edu/data/amazon/)]([https://jmcauley.ucsd.edu/data/amazon/](https://jmcauley.ucsd.edu/data/amazon/)), accessed on 31 July 2020) as the dataset for the experiment. This dataset contains user information, product information, ratings, reviews, and other data. The method proposed herein is divided into seven modules: data pre-processing module, aspect detection module, sentiment analysis module, semantic analysis module, user preference model, product evaluation model, and rating prediction module.  
First, we set the data filtering criteria for the product reviews of users to select the eligible users and reviews, and then conducted pre-processing. Regarding the aspect detection module, the aspects of the sentences in the reviews were analyzed to infer the wider aspects of the article and then build the aspect embedding vector. Sentiment analysis  
Figure 1: The overview of the proposed method.  
was used to calculate the sentiment intensity in each aspect of the reviews. The sentiment feature vector of the aspect was then built via the neural network method. The semantic analysis uses a hierarchical attention network (HAN) to calculate the weights of words and sentences in user and product reviews. The resulting weights are used to generate the aspect semantic vectors of the users and products. In the user preference model, the aspect vectors, aspect sentiment scores, and aspect semantic vectors, as extracted from user reviews, are integrated and fed into a user-based CNN to generate the user latent factors. Similarly, the product evaluation model generates product latent factors in the same way as the user preference model. Finally, the matrix factorization (MF) method was used to integrate the latent factors of users and products to predict the future products that users may be interested in.