摘要
随着越来越多的用户与服务参与到服务计算中,服务推荐变得日益重要,但个别用户的虚假评价降低了服务推荐结果的可信性和有效性。为此,提出一种新的服务推荐方法,在分析用户历史评价的基础上挖掘可疑评价,通过用户可信度的计算将恶意用户筛除。基于相似度计算可得到剩余各可信用户的相邻用户集合,并最终结合用户偏好确定候选服务进行综合评分以完成有效推荐。实验结果表明,在剔除恶意用户的情况下,该方法推荐的服务更为真实可靠,可有效提高推荐质量。
It is increasingly important to perform service recommendation as increasingly more users and services are involved in service computing,but some inauthentic service evaluations from a few users decrease the dependability and effectiveness of service recommendation.This paper proposes a service recommendation method based on trusted similar users.Suspicious evaluations are dug out from the history of the user evaluations,which are used for computing the degree of user reliability and subsequently removing the unreliable users.The neighbor sets of each trusted user are determined by computing the user similarity.According to the preference of any user,candidate services from neighbors are carefully overall evaluated and effectively recommended.Experimental results show that the method,in the case of rejecting a malicious user,recommends more reliable service and effectively improves the quality of recommendation.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第11期57-63,69,共8页
Computer Engineering
基金
国家自然科学基金(61402241)
关键词
服务推荐
用户可信度
恶意用户
可信用户
用户相似度
service recommendation
user reliability degree
malicious user
trusted user
user similarity
