With the rapid development of the internet, users tend to refer to the rating scores or review opinions on social platforms. Most recommendation systems use collaborative filtering (CF) methods to recommend items based on users’ ratings. The rating-based CF methods do not consider users’ review opinions on different aspects of items. The accuracy of the rating predictions can be effectively improved by considering the latent semantics and various aspects of user reviews. In this paper, a novel rating prediction method is proposed according to an attention-based gated recurrent unit (GRU) deep learning model with semantic aspects. A two-phase method is proposed herein; it combines the word attention mechanism and review semantics to extract aspect features from user preferences. In the first phase, a bidirectional GRU neural network is adopted according to word attention in order to extract important words from users’ reviews. In the second phase, we split users’ reviews into words, and generate the aspect-based attention semantic vectors from these reviews based on Latent Dirichlet Allocation and the attention weights of the chosen words. The XGBoost method is then adopted to predict user preference ratings based on the aspect-based attention semantic vectors. The experimental results show that the proposed method outperforms traditional prediction methods and effectively improves the accuracy of predictions.

随着互联网的快速发展，用户倾向于参考评分分数或在社交平台上查看意见。大多数推荐系统使用协作过滤（CF）方法来基于用户的评分推荐项目。基于评分的CF方法不考虑用户对项目不同方面的评论意见。通过考虑潜在语义和用户评论的各个方面，可以有效地提高评分预测的准确性。针对具有语义方面的基于注意力的门控递归单元（GRU）深度学习模型，提出了一种新的评分预测方法。本文提出了一种两阶段方法。它结合了单词注意机制和复习语义，以从用户偏好中提取方面特征。在第一阶段，为了吸引用户评论，从单词关注度出发采用双向GRU神经网络。在第二阶段，我们将用户的评论分为单词，然后根据潜在Dirichlet分配和所选单词的注意力权重，从这些评论中生成基于方面的注意语义矢量。然后，采用XGBoost方法基于基于方面的注意力语义向量来预测用户偏好等级。实验结果表明，该方法优于传统的预测方法，有效地提高了预测的准确性。并根据潜在Dirichlet分配和所选单词的注意权重，从这些评论中生成基于方面的注意语义矢量。然后，采用XGBoost方法基于基于方面的注意力语义向量来预测用户偏好等级。实验结果表明，该方法优于传统的预测方法，有效地提高了预测的准确性。并根据潜在Dirichlet分配和所选单词的注意权重，从这些评论中生成基于方面的注意语义矢量。然后，采用XGBoost方法基于基于方面的注意力语义向量来预测用户偏好等级。实验结果表明，该方法优于传统的预测方法，有效地提高了预测的准确性。