CAJ | 학술논문

现有微博好友推荐算法使用的用户信息比较单一,不能充分利用微博用户信息来刻画用户特征,导致推荐效果不理想。为解决该问题,在综合分析用户标签信息、内容信息、交互信息以及社交拓扑信息的基础上,通过计算主题相关度、兴趣相关度、用户亲密度进行特征挖掘,并采用K最近邻分类算法为目标用户进行微博好友推荐。在新浪微博真实用户数据集上的实验结果表明,该算法的准确率、召回率、F1度量值分别为16.5%,26.8%,19.2%,推荐效果优于基于内容的推荐算法和基于社会过滤的推荐算法。
현유미박호우추천산법사용적용호신식비교단일,불능충분이용미박용호신식래각화용호특정,도치추천효과불이상。위해결해문제,재종합분석용호표첨신식、내용신식、교호신식이급사교탁복신식적기출상,통과계산주제상관도、흥취상관도、용호친밀도진행특정알굴,병채용K최근린분류산법위목표용호진행미박호우추천。재신랑미박진실용호수거집상적실험결과표명,해산법적준학솔、소회솔、F1도량치분별위16.5%,26.8%,19.2%,추천효과우우기우내용적추천산법화기우사회과려적추천산법。
Existing microblog friend recommendation algorithms just use relatively simple information about user to make recommendation, they can not take full advantages of the rich user information and describes the profound characteristics of the user, and the result which in recommendation effect is not very satisfactory. Aiming at these problems,this paper presents an algorithm that uses the user’ s tag,user’ s content,the interaction between users and the user’ s social topological information. It mines and computes topic relevancy,interest relevancy,intimacy score from these information and then uses K Nearest Neighbor( KNN) classification algorithm to recommend. Through experiments on Sina microblog user data set, and the results show that the algorithm can achieve better recommendation effect than content-based algorithm and social filtering-based algorithm, and the highest accuracy, recall, F1 measure respectively reach 16 . 5%,26 . 8%,19 . 2%.