基于增加相似度系数的加权二部图推荐算法  

Weighted Bipartite Network Recommendation Algorithm Based on Increasing Similarity Coefficient

作　　者：李镇东[1,2] 罗琦[1,2] 施力力 

机构地区：[1]南京信息工程大学信息与控制学院江苏省气象能源利用与控制工程技术研究中心,南京210044 [2]南京信息工程大学信息与控制学院江苏省大气环境与装备技术协同创新中心,南京210044

出　　处：《计算机科学》2016年第7期259-264,共6页Computer Science

基　　金：国家自然科学基金资助项目(61174077)资助

摘　　要：基于二部图的推荐算法是个性化推荐领域的一个研究热点,其中,如何科学地利用用户的评分资源,在评分数据不全的情况下对目标用户进行准确高效的推荐是研究难点,也因此受到众多学者的关注。因此,提出了一种以单调饱和函数为权,利用目标用户和其他项目共同评分个数相对用户总数均值的正切值作为传统相似度系数的推荐算法;同时,对调整系数后的相似度进行降序排列,利用前K个最近邻居集对目标用户进行推荐。实验结果表明,改进后的算法提高了推荐的准确性,降低了复杂度。The recommendation algorithm based on bipartite networks is a research hotspot in the personalized recom- mendation system, while the difficulty of research is how to make use of the users＇ evaluation resources scientifically to work out an efficient and accurate recommendation for target users in the absence of rating data. Meanwhile, it has received sufficient attention of scholars. Therefore, a new recommendation algorithm was put forward with the monoton- ous saturation function as weight,and tangent of target users and other projects＇ common rating numbers against the mean value of total users is used as traditional similarity coefficient. At the same time, after the coefficient gets adjusted, the similarity will be sorted in descending order, the set of the first K nearest neighbors of which can be utilized for target users＇ recommendation. The experimental results prove that the revised algorithm improves the accuracy of re- commendation and reduces its complexity.

关 键 词：个性化推荐 加权二部图 单调饱和 准确性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]