A recommender system utilizes information filtering techniques to help users obtain accurate information effectively and efficiently. The existing recommender systems, however, recommend items based on the overall ratings or the click-through rate, and emotions expressed by users are neglected. Conversely, the cold-start problem and low model scalability are the two main problems with recommender systems. The cold-start problem is encountered when the system lacks initial rating. Low model scalability indicates that a model is incapable of coping with high-dimensional data. These two problems may mislead the recommender system, and thus, users will not be satisfied with the recommended items. A hybrid recommender system is proposed to mitigate the negative effects caused by these problems. Additionally, ontologies are applied to integrate the extracted features into topics to reduce dimensionality. Topics mentioned in the items are displayed in the form of a topic map, and users can refer to these similar items for further information.

推荐系统利用信息过滤技术来帮助用户有效和高效地获得准确的信息。然而，现有的推荐器系统基于总体评级或点击率来推荐项目，并且忽略了用户表达的情绪。相反，冷启动问题和低模型可伸缩性是推荐系统的两个主要问题。系统缺乏初始额定值时会遇到冷启动问题。低模型可伸缩性表明模型无法处理高维数据。这两个问题可能会误导推荐系统，因此，用户对推荐项目将不满意。为了减轻由这些问题引起的负面影响，提出了一种混合推荐系统。另外，应用本体将提取的特征集成到主题中以减少维度。项目中提到的主题以主题图的形式显示，用户可以参考这些类似的项目以获取更多信息。