Abstract: With the popularity of microblog,problems such as information overload are increasingly prominent.How to help users find the microblog they need quickly and accurately has become an urgent problem to be solved.Although microblog recommendation based on collaborative filtering technology and LDA can achieve certain accuracy,it can not solve the problems of genernal classification of content and the disadvantages when LDA model is used to deal with short texts.Therefore,this paper proposes a personalized microblog recommendation model integrating content similarity and multi-feature computing.Firstly,the content similarity between user and microblog is calculated based on word2vec.Then,according to the characteristics such as time,number of likes,comments and reposts,the freshness and popularity of microblog are calculated.Finally,the content similarity,freshness and popularity of microblog are comprehensively considered to calculate its ranking score,so as to realize users’ personalized microblog recommendation.This model considers recommendation from the perspective of content similarity,avoiding the above problems and making the recommendation results more accurate in semantics.Experimental results show that the proposed model has good performance in accuracy,recall rate and F-measure,in particular,the accuracy has been significantly improved by about 10%,and F-Measure is increased by about 5%,and the validity of the model is proved.

Key words: Freshness, Mircroblog, Popularity, Similarity, word2vec