基于RBLDA模型和交互关系的微博标签推荐算法

随着互联网技术的发展, 个性化标签推荐系统在海量信息或资源过滤中起着重要的角色. 在新浪微博平台中, 用户可以自主的给自己添加标签来表明自己的兴趣爱好. 同时, 用户也可以通过标签来搜索与自己兴趣爱好相似的用户. 针对新浪微博中大部分用户没有添加标签或添加标签数目较少的问题, 提出了一种基于RBLDA模型和交互关系的微博标签推荐算法, 它首先利用RBLDA模型来产生用户的初始标签列表, 然后再结合用户的交互关系而形成的交互图来预测用户标签的算法. 通过在新浪微博真实数据集上的实验发现, 该方案与传统的标签推荐算法相比, 取得了良好的实验效果.

RBLDA Model and Interaction Relation Algorithm for User Tags Recommendation in Microblog

With the development of internet technology, the personalized tag recommendation system plays an important role in information or resources filtering. In Sina microblog website, an user can freely tag himself to indicate his interests. Meanwhile, users can also search other users who have the similar interests through tags. For the issue that there are no tags or few tags for the most users in Sina microblog website, an algorithm based on RBLDA model and users' interaction graph for tags recommendation is proposed in this paper. The algorithm utilizes the RBLDA model to produce the intial list of tags, and combines with users' interaction graph generated from actions of interaction between users to predict the final tags. The experimental results carried on some real data sets show that the proposed method performs better than traditional tag recommendation algorithms in comparison.