基于多权重相似度的随机漫步推荐算法  

Research on random walk recommendation algorithm based on multi-weight similarity

作　　者：邹洋 吴和成[1] 赵应丁[2] 姜允志 Zou Yang;Wu Hecheng;Zhao Yingding;Jiang Yunzhi(College of Economics&Management,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China;College of Software,Jiangxi Agricultural University,Nanchang 330045,China;School of Mathematics&Systems Science,Guangdong Polytechnic Normal University,Guangzhou 510540,China)

机构地区：[1]南京航空航天大学经济与管理学院,南京211106 [2]江西农业大学软件学院,南昌330045 [3]广东技术师范大学数学与系统科学学院,广州510540

出　　处：《计算机应用研究》2020年第11期3267-3270,3296,共5页Application Research of Computers

基　　金：国家自然科学青年科学基金资助项目(61702118)。

摘　　要：在传统个性化推荐算法的基础上,提出了一种基于多权重相似度的随机漫步推荐算法。为了解决传统协同过滤算法中忽略社交网络、热门项目以及共同评分项目之间影响等问题,通过引入万有引力公式计算社交网络中的用户相似度,并对传统协同过滤算法中的相似度进行改进,采用权重因子结合这两者相似度,最后开拓性地结合随机漫步算法进行商品推荐。实验结果表明,提出算法具有比其他推荐算法更好的推荐性能。Based on the traditional personalized recommendation algorithm,this work presented a random walk recommendation algorithm using multi-weight similarity.In order to make up the absence of the influence among social networks,popular items and common scoring items in traditional collaborative filtering algorithms,this paper introduced a gravity formula,which could calculate user similarity in social networks.In addition,this work used weight factors to combine the gravity formula method with the traditional collaborative filtering algorithms.Furthermore,it also added the random walk algorithm for recommendation functions.The experimental results show that the proposed algorithm has better recommendation performance by comparing with other kinds of recommendation algorithms.

关 键 词：推荐算法 万有引力 随机漫步算法 个性化推荐 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]