基于LDA模型的多角度个性化微博推荐算法  

Multi-angle Personalized Microblog Recommendation Algorithm Based on LDA Model

作　　者：孙玉洁[1] 秦永彬[1] 

机构地区：[1]贵州大学计算机科学与技术学院,贵阳550025

出　　处：《计算机工程》2017年第4期177-182,共6页Computer Engineering

基　　金：国家自然科学基金(61262006;61540050);贵州省重大应用基础研究项目(黔科合JZ字[2014]2001);贵州省科技厅联合基金(黔科合LH字[2014]7636号)

摘　　要：通过基于概率的主题挖掘模型隐含狄利克雷分布(LDA)挖掘用户兴趣主题,是目前最常用的用户兴趣主题挖掘方法。为进一步改善用户体验,推荐其感兴趣且质量好、新鲜度高的微博,提出一种新的多角度个性化微博推荐算法。通过微博发布时间、转发数、评论数等特征计算微博重要度,利用LDA模型生成的用户-主题矩阵以及主题-词汇矩阵计算用户对微博的兴趣度,综合考虑微博本身的重要度以及用户对微博的兴趣度对微博进行评分,根据评分结果推荐微博。实验结果表明,该算法与主题模型相结合可有效够提高微博推荐的精准度。Using Latent Dirichlet Allocation（ LDA） to mine topics which users are interested in is the most popular topicmining method. In order to improve users＇ experience and recommend fresh microblog which users are interested,this paper puts forward a multi-angle microblog recommendation algorithm which is based on the LDA model,then the author takes advantage of microblog＇s publish time,forwards,comments and other features to calculate the microblog＇s importance and uses the user-topic matrix and topic-word matrix generated by the LDA model to calculate the users＇ interest in microblog.It tries to score the microblog by comprehensively considering the microblog＇ s importance and the users＇ interest to the microblog. Microblog is recommended according to the score. Experimental results showthat the microblog recommendation algorithm can effectively improve the accuracy of microblog recommendation.

关 键 词：微博 个性化推荐 隐含狄利克雷分布模型 推荐算法 评分预测 Top-N推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]