基于综合相似度和社交标签的推荐算法  

Recommendation Algorithm Based on Synthetic Similarity and Social Tag

作　　者：时念云[1] 张芸 马力[1] SHI Nian-Yun Zhang Yun MA Li(College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Chin)

机构地区：[1]中国石油大学(华东)计算机与通信工程学院,青岛266580

出　　处：《计算机系统应用》2017年第10期178-183,共6页Computer Systems & Applications

摘　　要：针对传统个性化推荐方法所面临的冷启动、数据稀疏等问题,本论文结合了项目组的前期研究,在综合考虑用户特征和用户信任度的基础上,引入了用户兴趣,形成综合相似度.针对目前推荐系统中评分数据较少的问题,论文结合了社交标签,丰富了推荐数据.首先利用综合相似度,找到用户的相似近邻,并将相似近邻所标注的标签形成一个标签集.其次利用基于标签的推荐算法,产生最终的推荐列表.实验结果表明,该算法能够有效提高推荐的准确率和召回率.The traditional methods of personalized recommendation are faced with the problems of sparse data and cold start. This paper combines the previous research of the project team and introduces the user interest to form the comprehensive similarity, based on the comprehensive consideration of user characteristics and user trust degree. At the same time, this paper uses the social tags which enrich the recommendation data to solve the problem of sparse data in current recommendation system. Firstly, the similarity degree is used to find the similar neighbors of the users and form a tag set by labeling the similar neighbors. Secondly, a tag-based recommendation algorithm is used to generate the final recommendation list. The experimental results show that the proposed algorithm can effectively improve the accuracy of recommendation and the recall rate.

关 键 词：用户特征 信任度 冷启动 用户兴趣 社交标签 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]