基于降噪关系正则化的微博用户标签推荐*

现有微博用户标签推荐方法大多依靠好友关系或内容进行推荐,并不能解决微博中存在的从众关系(噪音关系)及用户标签稀疏问题.因此,文中提出基于降噪关系正则化的微博用户标签推荐算法.通过LDA对用户的博文进行主题分析,衡量用户好友兴趣相似度,降低无共同兴趣的好友对目标用户的影响.将得到的降噪关系作为正则化项引入到用户标签非负矩阵分解模型中,解决用户标签稀疏问题.通过拉格朗日乘子法和KKT条件对模型进行优化和约束,最终得到近似的用户标签矩阵,为用户进行标签推荐.实验表明文中算法推荐质量较优.

Microblog User-Tag Recommendation Algorithm Based on Noise Reduction Relation Regularization

The existing microblog user-tag recommendation methods mostly rely on friends relationship or content to realize recommendation, and the bandwagon relationship existing in microblog (noise problem) can not be discovered and the user label sparse problem is not solved. Therefore, a microblog user-tag recommendation algorithm based on noise reduction relation regularization is presented. The similarity of the user′s and his friends′ interests is measured by the micro-blog theme of users extracted by LDA to reduce the influence of those friends without interests in common with the target user. The noise reduction relationship is taken as the regularization item and it is introduced into user-tag nonnegative matrix factorization model to solve the user-tag sparse problem. The model is optimized and constrained via the Lagrange multiplier method and the KKT conditions, and finally the approximate user-tag matrix for recommended users′ tag is obtained. The experimental results show the proposed method exposes the high quality in recommendation.