## 5 Conclusions and Future Studies  
Due to the extremely large amount of review data on the Internet, users need to spend a lot of time analyzing such data, which reduces the efficiency of purchasing decisions. A recommender system can automatically analyze user preferences and recommend items that may be of interest to future users to solve the problem of information overload. In addition, the textual reviews may imply users' preferences for different topics or aspects, and the sentiment and semantics implied by the text may also express user preferences. In addition to the rating values, more user preferences and implied product features can be extracted from reviews. By accurately analyzing user preferences, the accuracy of rating prediction can be improved. Therefore, this study proposed the aspect-based deep learning rating prediction method (ADLRP), which consists of four main components: aspect detection, sentiment analysis, semantic analysis, and rating prediction. By analyzing user and product reviews, we built feature vectors, including aspect, aspect sentiment, and aspect semantics, which were integrated and trained by a CNN to generate user latent factors and product latent factors. Finally, this study used the MF method to predict the ratings of products that users have not rated. The experimental results show that the best prediction accuracy was achieved by using aspect, aspect sentiment, and aspect semantic features for rating predictions; this also means that integrating multiple features can help build more accurate user preference features and product evaluation features, thereby improving the accuracy of rating prediction, which enables merchants to better understand users' preferences and provide them with more accurate product recommendations.  
As the aspect analysis results affect the accuracy of rating prediction, this study will be extended in the future regarding aspect analysis. For example, we will analyze the importance of different aspects to users or products and set the corresponding weights automatically. In addition, semantic features may affect the results of rating prediction. In the future, this study will adopt different methods to analyze the semantic features in reviews, such as Embeddings from Language Models (ELMo) or BERT. In the future, we will analyze the importance of semantic features and sentiment features to users and provide different weights to build an accurate user preference feature vector. As there may be relationships between user behaviors or products, the graphic neural network method will be used to analyze the relations between users and products. By integrating the results of the review analysis and relationship analysis, prediction accuracy will be improved.  
Conceptualization, methodology and writing--review and editing, C.-H.L.; methodology, software, and validation, K.-C.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology of Taiwan, grant number MOST 109-2410-H-033-011-MY2. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[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). The authors declare no conflict of interest.