## 1 Introduction  
With the rapid development of Internet technology, the Internet has brought people much information while also causing the problem of information overload. This makes it difficult for people to quickly and accurately select the parts they are interested in from massive amounts of information. Collaborative filtering (CF) [1; 2; 3] technology is one of the methods used to solve this problem. However, CF cannot accurately predict the user ratings under data sparsity. To alleviate this problem, researchers use reviews to predict user ratings. For example, [PERSON] et al. [4] proposed the DeepCoNN model, as shown in Figure 1. First, CNN [5; 6] is used to learn the user and item representations from reviews, then FM [7; 8] is used to predict the ratings, resulting in excellent performance and triggering a research boom in using reviews to predict ratings. The performance of the DeepCoNN model depends on the target reviews. The target review is the review that the target user writes on the target item when the recommendation system predicts the target user's rating for the target item. However, in real life, there are two issues with target reviews, as shown in Figure 2. The two problems are: (1) inconsistency between the target review and the rating; (2) difficulty in obtaining the target review promptly. In terms of the difficulty in obtaining target reviews, [PERSON] et al. [9] pointed out that target reviews should not be present during prediction, but they can be used for model training and learning. They proposed the TransNets [9] model, which consists of a source network and a target network. The source network learns to predict features from the user and item review documents. The target network takes target reviews as input, first learning the representation of predictive features, and then guiding the source network to learn predictive features. This allows the TransNets model to avoid using target reviews during the prediction process. Although the TransNets model alleviates the problem of the difficulty in obtaining target reviews promptly, the simple structure of the target network and the complexity of the task make it difficult to effectively guide the learning of the source network. In addition, the inconsistency between the target reviews and the ratings is not considered. In terms of inconsistent target reviews and ratings, [PERSON] et al. proposed the D-Attn [10] model based on the DeepCoNN, which introduced local and global attention mechanisms to find more reviews and words related to the rating in user (item) reviews and increase their corresponding weights, thereby, improving the recommendation performance. [PERSON] et al. subsequently proposed the NARRE [11] model based on the DeepCoNN, which introduced a review attention mechanism to remove reviews unrelated to the predicted rating and improve the recommendation performance. Subsequently, [PERSON] et al. proposed the MPCN [12] model, which selected several relevant reviews first through a multi-pointer scheme and a joint attention mechanism, and then constructed a user-item matrix. They retained the most  
Figure 1: The architecture of the DeepCoNN model. Figure 2: The problems encountered in practical life by recommendation models based on reviews. relevant part of the rating through a hierarchical filtering approach, thereby improving the recommendation performance. [PERSON] et al. proposed the MPCAR [13] model based on MPCN to suppress the selection of irrelevant reviews for the target item by introducing ID information to enrich the feature representation. However, they ignored the difficulty of obtaining target reviews and either linearly fused the ID information or concatenated multiple vectors to fuse the ID information, making it difficult to dynamically balance the information between the target reviews and the ID. In order to solve the above problem, this paper proposed a Recommendation algorithm that Simulates the generation of target review semantics and fuses ID Information (RSII).