改进填补法和多权重相似度相结合的推荐算法  

Recommendation algorithm based on filling method and multi-weight similarity

作　　者：邹洋 吴和成[1] 姜允志 赵应丁[3] Zou Yang;Wu Hecheng;Jiang Yunzhi;Zhao Yingding(College of Economic&Management,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China;School of Mathematics&Systems Science,Guangdong Polytechnic Normal University,Guangzhou 510540,China;College of Software,Jiangxi Agricultural University,Nanchang 330045,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]广东技术师范大学数学与系统科学学院,广州510540 [3]江西农业大学软件学院,南昌330045

出　　处：《计算机应用研究》2020年第12期3578-3581,3598,共5页Application Research of Computers

基　　金：国家自然科学青年基金资助项目(61702118);广东省教育厅青年创新人才项目(自然科学)(2016KQNCX089)。

摘　　要：针对传统推荐算法中存在的数据稀疏性问题,国内外许多研究人员都提出了相应的推荐算法。然而,在个性化推荐方面,其中大多数并没有取得很好的推荐效果。因此,提出改进填补法和多权重相似度相结合的推荐算法,该算法首先采用改进填补法填充缺失值并对数据降维,接着分别计算社交网络用户信任度和改进的二部图用户关联度,最后采用多权重因子将这两者相似度进行结合。基于此,该算法根据相似度高低获取邻居用户并对目标用户进行商品推荐。实验结果表明,在数据稀疏性以及个性化推荐情况下,该算法的平均绝对误差(MAE)优于其他推荐方法。In order to solve the problem of data sparsity in traditional recommendation algorithms,many researchers at home and abroad have proposed corresponding recommendation algorithms.However,most of these algorithms have not achieved good recommendation results in personalized recommendation.Therefore,this paper proposed a recommendation algorithm based on improved filling method and multi-weight similarity.Firstly,the algorithm filled missing values and reduced data dimension by improved filling method,then calculated user trust degree and user association degree of bipartite graph respectively,and finally used multi-weight factor to combine the two similarities.Based on this,this algorithm obtained neighbor users according to similarity and made recommendation to target users.The experimental results show that the MAE of proposed algorithm is superior to other recommendation methods in the case of sparse data and personalized recommendation.

关 键 词：推荐算法 二部图关联度 社交网络相似度 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]