一种基于用户动态兴趣和社交网络的微博推荐方法

针对为微博用户推荐符合其兴趣取向的个性化微博信息的问题，结合LDA主题模型，提出了一种基于用户动态兴趣和社交网络（DISN）的微博推荐方法.DISN方法首先引入时间函数，推断出用户的兴趣向量，通过对新发布的微博数据内容进行聚类分组，以用户兴趣向量筛选与用户最匹配的分组，随后以网格索引的形式对选定的分组中微博进行查询，计算微博发布者被目标用户关注的可能性并进行排序，最终形成推荐列表.实验验证了DISN方法较之传统方法更具有效性和高效性.

Personalized Microblogging Recommendation Based on Dynamic Interests and Social Networking of Users

To recommend useful microblogs that match users' interests and likes effectively,an approach in which the dynamic interests and social networking (DISN) of users are seamlessly integrated based on LDA model is proposed.The approach infers the interest vector of users better by using time function and groups the new published microblogs by clustering method and gets the best matching groups with users' interest vector.Then DISN traverses the selected groups by grid querying approach and matches the microblogs with publishers' probabilities of being followed and sorts the result.Finally the personalized microblogging recommendation is achieved.Experimental results show that DISN is more effective and efficient than the traditional models.