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A Trusted Service Selection Method Based on User's Personality Feature and Service Recommendation

机译:基于用户个性特征和服务推荐的可信服务选择方法

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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.
机译:鉴于当前服务选择方法中基于QoS的服务选择方法与服务请求者的个性属性特征以及基于协作过滤的服务选择方法较少相关,服务提供商基于特征针对服务请求者的个性,本文通过定义用户的相似性和领域相关性来描述用户的相关性,并使用可信的度量理论给出推荐可信度的计算方法。利用层次分析法(AHP)确定各个相关因子的权重,提出了基于协同过滤服务选择信任模型(SSTM)的可信服务选择模型。仿真结果表明,该模型可以有效提高服务选择效率,抵抗恶意反馈的攻击。主要有以下两个创新:首先,介绍用户相关性以反映网络环境下两个用户(服务请求者)之间的接近程度;在预测过程中将用户的个性属性特征应用于服务提供商的声誉值,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,将用户的相关性和推荐信誉有机地结合起来,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,使被预测的服务提供商的信誉更加可靠,并有效抵御恶意用户的反馈。

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