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.
作者
邹洋
吴和成
赵应丁
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)
出处
《计算机应用研究》
CSCD
北大核心
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)。
关键词
推荐算法
万有引力
随机漫步算法
个性化推荐
recommendation algorithm
gravitation
random walk algorithm
personalized recommendation
