面向智慧社区基于可信联盟的服务推荐算法
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摘要
针对现有推荐算法同等看待每个用户评价信息的问题,提出一种面向智慧社区的基于可信联盟的服务推荐算法。引入用户的信誉度和服务使用频率,改进传统相似度计算公式,建立基于用户信任模型的信任关系。在此基础上,面向智慧社区用户,引入社区因子,构建可信联盟,从而对目标用户进行个性化推荐。实验结果表明,与基于云模型的链式推荐等算法相比,该推荐算法的精确度更优。
Aiming at the problem that the existing recommendation algorithms treat each user's evaluation information equally,a service recommendation algorithm based on trusted alliance for intelligent community is proposed. By introducing user reputation and service usage frequency,the traditional similarity calculation formula is improved,and the trust relationship based on user trust model is established. On this basis,the community factor is introduced and the trusted alliance is constructed for the intelligent community users,and then the personalized recommendation for the target users is carried out. Experimental results show that the accuracy of the proposed algorithm is better than that of the chain recommendation algorithm based on cloud model.
Aiming at the problem that the existing recommendation algorithms treat each user's evaluation information equally,a service recommendation algorithm based on trusted alliance for intelligent community is proposed. By introducing user reputation and service usage frequency,the traditional similarity calculation formula is improved,and the trust relationship based on user trust model is established. On this basis,the community factor is introduced and the trusted alliance is constructed for the intelligent community users,and then the personalized recommendation for the target users is carried out. Experimental results show that the accuracy of the proposed algorithm is better than that of the chain recommendation algorithm based on cloud model.
引文
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[2]朱锐,王怀民,冯大为.基于偏好推荐的可信服务选择[J].软件学报,2011,22(5):852-864.
[3]刘电霆,张全永.一种改进的基于云模型的链式推荐算法[J].广西大学学报(自然科学版),2015,40(6):1501-1507.
[4]刘群,陈阳.一种融合信任度和相似度的推荐算法[J].重庆邮电大学学报(自然科学版),2014,26(4):536-540.
[5]钟晓宇,刘宴兵,肖云鹏.一种基于相似社团和节点角色划分的社交网络用户推荐方案[J].重庆邮电大学学报(自然科学版),2016,28(4):525-532.
[6]WANG Z,LIAO J,CAO Q,et al.Friendbook:a semantic-based friend recommendation system for social networks[J].IEEE Transactions on Mobile Computing,2015,14(3):538-551.
[7]ADENIYI D A,WEI Z,YONGQUAN Y,et al.Automated Web usage data mining and recommendation system using K-Nearest Neighbor(KNN)classification method[J].Applied Computing and Informatics,2016,12(1):90-108.
[8]张鹏,葛小青.融合标签相似度的k近邻Slope One算法[J].重庆邮电大学学报(自然科学版),2016,28(4):518-524.
[9]CARULLO G,CASTIGLIONE A,SANTIS A D,et al.A triadic closure and homophily-based recommendation system for online social networks[J].World Wide Webinternet and Web Information Systems,2015,18(6):1579-1601.
[10]王永,万潇逸,陶娅芝,等.基于K-medoids项目聚类的协同过滤推荐算法[J].重庆邮电大学学报(自然科学版),2017,29(4):521-526.
[11]王瑞琴,蒋云良,李一啸,等.一种基于多元社交信任的协同过滤推荐算法[J].计算机研究与发展,2016,53(6):1389-1399.
[12]刘慧婷,熊瑞瑞,赵鹏.基于信任关系重建和社交网络传递的推荐算法[J].计算机工程,2016,42(1):174-179.
[13]卢凤,李海荣,韩艳.基于时空相似度感知的Web服务QoS协同过滤推荐[J].计算机工程,2017,43(4):28-33.
[14]YIGIT M,BILGIN B E,KARAHOCA A.Extended topology based recommendation system for unidirectional social networks[J].Expert Systems with Applications,2015,42(7):3653-3661.
[15]GEVA T,ZAHAVI J.Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news[J].Decision Support Systems,2013,57(1):212-223.
[16]ZHANG M,RANJAN R,MENZEL M,et al.An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints[J].IEEE Systems Journal,2015(99):1-11.
[17]刘树栋,孟祥武.基于位置的社会化网络推荐系统[J].计算机学报,2015,38(2):322-336.
[18]钱晓捷,张路一.融合评分结构特征与偏好距离的协同过滤推荐算法[J].计算机工程,2017,43(5):185-190.