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.

本文提出了一种基于更新信念系统的偏好协作测量框架，旨在减少在偏好测量过程中的人工干预，并提升算法的准确性和效率。通过引入规则间距离和平均内部距离，开发了PRA算法，以最小信息损失率发现用户共同的偏好，并通过信念系统更新来提高测量的有效性。实验结果表明，所提出的IMCos和IMCov算法在准确性和效率方面显著优于其他先进算法。

基于信念系统的上下文偏好协作测量框架