基于用户行为特征的多维度文本聚类

传统多维度文本聚类一般是从文本内容中提取特征，而很少考虑数据中用户与文本的交互信息（如：点赞、转发、评论、关注、引用等行为信息），且传统的多维度文本聚类主要是将多个空间维度线性结合，没能深入考虑每个维度中属性间的关系。为有效利用与文本相关的用户行为信息，提出一种结合用户行为信息的多维度文本聚类模型（MTCUBC）。根据文本间的相似性在不同空间上应该保持一致的原则，该模型将用户行为信息作为文本内容聚类的约束来调节相似度，然后结合度量学习方法来改善文本间的距离，从而提高聚类效果。通过实验表明，与线性结合的多维度聚类相比，MTCUBC模型在高维稀疏数据中表现出明显的优势。

Multi-dimensional text clustering with user behavior characteristics

Traditional multi-dimensional text clustering generally extracts features from text contents, but seldom considers the interaction information between users and text data, such as likes, forwards, reviews, concerns, references, etc. Moreover, the traditional multi-dimension text clustering mainly integrates linearly multiple spatial dimensions and fails to consider the relationship between attributes in each dimension. In order to effectively use text-related user behavior information, a Multi-dimensional Text Clustering with User Behavior Characteristics (MTCUBC) was proposed. According to the principle that the similarity between texts should be consistent in different spaces, the similarity was adjusted by using the user behavior information as the constraints of the text content clustering, and then the distance between the texts was improved by the metric learning method, so that the clustering effect was improved. Extensive experiments conduct and verify that the proposed MTCUBC model is effective, and the results present obvious advantages in high-dimensional sparse data compared to linearly combined multi-dimensional clustering.