Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid personalized recommendation model that extracts user preferences by analyzing user review content in different sentiment polarity at the sentence level, based on jointly applying user-item score matrices and dimension reduction methods. A novel voting mechanism is also designed based on positive preferences from the neighbors of the target user to directly generate the recommendation results. The experimental results of testing the proposed model with a real-world data set show that our proposed model can achieve better recommendation effects than the representative recommendation algorithms. In addition, we demonstrated that fine-grained emotion recognition has good adaptability to a sparse rating matrix with a reasonable and good performance.

个性化推荐系统可以帮助用户从大量数据中过滤出冗余信息。先前的相关研究着重于通过分析来自评论社区的文本来学习用户偏好，而没有探索遇到冷启动问题的详细情感极性。为了弥补这一研究空白，我们提出了一种混合个性化推荐模型，该模型基于联合应用用户项目得分矩阵和降维方法，通过分析句子级别不同情感极性的用户评论内容来提取用户偏好。还基于目标用户邻居的积极偏好设计了一种新颖的投票机制，以直接生成推荐结果。用真实数据集测试提出的模型的实验结果表明，与具有代表性的推荐算法相比，我们提出的模型可以实现更好的推荐效果。此外，我们证明细粒度的情感识别对稀疏等级矩阵具有良好的适应性，并且具有合理且良好的性能。