To reduce the human intervention in the preference measure process,this article proposes a preference collaborative measure framework based on an updated belief system,which is also capable of improving the accuracy and efficiency of preferen-ce measure algorithms.Firstly,the distance of rules and the average internal distance of rulesets are proposed for specifying the relationship between the rules.For discovering the most representative preferences that are common in all users,namely common preference,a algorithm based on average internal distance of ruleset,PRA algorithm,is proposed,which aims to finish the discoveryprocess with minimum information loss rate.Furthermore,the concept of Common belief is proposed to update the belief system,and the common preferences are the evidences of updated belief system.Then,under the belief system,the proposed belief degree and deviation degree are used to determine whether a rule confirms the belief system or not and classify the preference rules into two kinds(generalized or personalized),and eventually filters out Top-K interesting rules relying on belief degree and deviation degree.Based on above,a scalable interestingness calculation framework that can apply various formulas is proposed for accurately calculating interestingness in different conditions.At last,IMCos algorithm and IMCov algorithm are proposed as exemplars to verify the accuracy and efficiency of the framework by using weighted cosine similarity and correlation coefficients as belief degree.In experiments,the proposed algorithms are compared to two state-of-the-art algorithms and the results show that IMCos and IMCov outperform than the other two in most aspects.

翻译：为减少偏好度量过程中的人工干预，本文提出一种基于更新信念系统的偏好协同度量框架，该框架能够同时提升偏好度量算法的准确性与效率。首先，本文提出规则距离与规则集平均内部距离以量化规则间关联关系。为发现所有用户共有的最具代表性的偏好（即共性偏好），提出一种基于规则集平均内部距离的PRA算法，该算法旨在以最小信息损失率完成发现过程。进一步地，提出共性信念概念以更新信念系统，并将共性偏好作为更新后信念系统的证据。随后，在信念系统下，利用所提出的置信度与偏离度判定规则是否确证信念系统，将偏好规则划分为两类（泛化型或个性化），并最终依据置信度与偏离度筛选出Top-K有趣规则。基于上述工作，提出可扩展的兴趣度计算框架，该框架可适配多种计算公式以精准计算不同情境下的兴趣度。最后，以加权余弦相似度和相关系数作为置信度，提出IMCos算法与IMCov算法作为范例，验证框架的准确性与效率。实验中，将所提算法与两种前沿算法进行对比，结果表明IMCos与IMCov在多数性能指标上优于对比算法。