基于社会化标签挖掘的微博内容推荐方法研究

Micro-blog Content Recommendation Method Based on Social Tag Mining

【目的/意义】针对当前微博内容推荐方法存在的用户标签偏少、标签语义缺乏和用户-标签矩阵高维稀疏等导致的推荐准确性不高的问题,提出了一种基于社会化标签挖掘的微博内容个性化推荐方法。【方法/过程】首先,针对用户标签偏少的问题,借助微博内容进行用户标签扩充,形成初始的用户-标签矩阵表征用户兴趣偏好;然后,针对标签语义缺乏问题,通过标签语义映射和语义相关性挖掘,构建标签语义相似度矩阵进行用户-标签矩阵更新,使更新后的用户-标签矩阵融入标签间的语义相关性,既能解决用户-标签矩阵高维稀疏问题,又能更好地表征用户兴趣。【结果/结论】实验结果表明,该方法优于传统的基于社会化标签的推荐方法,能够有效提高微博内容推荐的精准性。【创新/局限】后续将重点研究用户间社交关系对个性化推荐的影响,探索融合用户社交关系的微博内容个性化推荐方法,实现更加精准的推荐。

【Purpose/significance】In view of the low accuracy of recommendation caused by the less user tags,lack of tag semantics and the high dimensional sparsity of user-tag matrix in the current microblog content recommendation methods,a personalized recommendation method based on social tag mining is proposed.【Method/process】First,aiming at the problem of less user tags,user tags are expanded with the help of microblog content to form the initial user-tag matrix to represent users’interests and preferences;then,for the lack of tag semantics,through the label semantic mapping,mining and semantic relevance,the tag semantic similarity matrix is constructed to update the user-tag matrix,making the updated user-tag matrix integrate into the semantic correlation between tags.It can solve the problem of users-tag high-dimensional sparse matrix,and better represent users’interests.【Result/conclusion】Experimental results show that this method is better than the traditional recommendation method based on social tags,and can effectively improve the accuracy of microblog content recommendation.【Innovation/limitation】In the future,we will focus on the impact of social relationship between users on personalized recommendation,and explore personalized recommendation method of microblog content integrating user social relationship,so as to achieve more accurate recommendation.