基于用户个性特征和服务推荐的可信服务选择方法

Weijin Jiang, Jiahui Chen, Qijie Feng
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引用次数: 1

摘要

针对当前服务选择方法中基于qos的服务选择方法对服务请求者的个性属性特征关注较少,而基于协同过滤的服务选择方法对服务提供者的个性特征关注较少的问题,本文根据服务请求者的个性特征,通过定义用户的相似性和领域相关性来描述用户相关性。并利用可信度测量理论给出了推荐信度的计算方法。利用层次分析法(AHP)确定各相关因素的权重,提出了一种基于协同过滤的可信服务选择信任模型(SSTM)。仿真结果表明,该模型能有效提高服务选择效率,抵御恶意反馈攻击。主要创新有两点:一是引入用户相关性,反映网络环境下两个用户(服务请求者)之间的密切程度;在预测过程中将用户的人格属性特征应用到服务提供商声誉值中,通过减小服务提供商的规模来提高服务选择的准确性。其次,将用户相关性和推荐可信度有机地结合起来,利用层次分析法确定服务选择指标体系中相关因素的权重,使预测的服务提供者的信誉更加可靠,有效抵御恶意用户反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Trusted Service Selection Method Based on User's Personality Feature and Service Recommendation
In view of the fact that the QoS-based service selection method in the current service selection method is less concerned with the personality attribute characteristics of the service requesters and the service selection method based on collaborative filtering, the service providers', Based on the characteristics of the personality of the service requester, this paper describes the user correlation by defining the similarity and domain relevance of the user, and the calculation method of the recommended credibility is given by using the credible measurement theory. Using the analytic hierarchy process (AHP) to determine the weight of each correlation factor, this paper proposes a credible service selection model based on collaborative filtering service selection trust model (SSTM). The simulation results show that the model can effectively improve the efficiency of service selection and resist the attack of malicious feedback. There are two major innovations as following: Firstly, to make a introduction of user relevance to reflect the degree of close between two users (service requester) under the network environment; to apply the user’s personality attribute characteristics to the service provider reputation value during the prediction, to improve the accuracy of service selection by reducing the size of service providers. Secondly, combining user relevance and recommendation credibility organically, using AHP to determine the weight of relevant factors in the service selection index system so that we can make the reputation of the predicted service provider more reliable and effectively resist the malicious user feedback.
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