计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 398-401.
魏慧娟,戴牡红
WEI Hui-juan, DAI Mu-hong
摘要: 为了解决在传统的协同过滤推荐算法中存在的相似性计算不准确的问题,并提高推荐系统的质量,提出一种用户相似度计算方法。在用户共同评分的基础上,该方法根据评分差值和时间特征来计算评分差值的信息熵;然后,利用用户评分差值的信息熵和评分项目属性计算出用户的相似度;最后,根据用户相似度计算出用户的最近邻居,以此预测目标项目的评分。实验结果表明,所提算法更加准确地实现了目标用户最近邻居的查找,有效地提高了推荐的准确性。
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[1]CAI Y,LEUNG H,LI Q,et al.Typicality-based collaborative filtering recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(3):766-779. [2]KALELI C.An entropy-based neighbor selection approach for collaborative filtering[J].Knowledge-Based Systems,2014,56(C):273-280. [3]汪静,印鉴,郑利荣,等.基于共同评分和相似性权重的协同过滤推荐算法[J].计算机科学,2010,37(2):99-104. [4]BOBADILLA J S,ORTEGA F,HERNANDO A,et al.A colla- borative filtering approach to mitigate the new user cold start problem[J].Knowledge-Based Systems,2012,26:225-238. [5]JIA D,ZHANG F,LIU S.A robust collaborative filtering re- commendation algorithm based on multidimensional trust model[J].Journal of Software,2013,8(1):11-18. [6]JU C,XU C.A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm [J].The Scientific World Journal,2013,2013(3):869658. [7]陈志敏,李志强.基于用户特征和项目属性的协同过滤推荐算法[J].计算机应用,2011,31(7):1748-1750. [8]HUANG M,SUN L,DU W.Collaborative filtering recommendation algorithm based on item attributes[C]∥2014 15th IEEE/ACIS International Conference on Software Engineering,Artificial Intelligence,Networking and Parallel/Distributed Computing (SNPD).IEEE,2014:101-106. [9]ZHANG J,PENG Q,SUN S,et al.Collaborative filtering re- commendation algorithm based on user preference derived from item domain features[J].Physica A:Statistical Mechanics and its Applications,2014,396(2):66-76. [10]PIAO C H,ZHAO J,ZHENG L J.Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce[J].Service Oriented Computing and Applications,2009,3(2):147-157. |
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