基于信任系统的条件偏好协同度量框架

doi:10.11896/jsjkx.190600152

摘要/Abstract

摘要： 为了减少偏好度量过程中的人为干预,同时提高偏好度量算法的效率和准确性,提出一种基于信任系统的偏好协同度量框架。首先,提出了规则间的距离和规则集的内部距离等概念来具体化规则之间的关系。在此基础上,提出了基于规则集平均内部距离的规则集聚合算法PRA,旨在保证损失最少信息的情况下筛选出最具代表性的全体用户的共同偏好,即共识偏好。之后,提出Common belief的概念和一种改进的信任系统,使用共识偏好作为信任系统的证据,在考虑用户一致性的同时还允许用户保留个性化信息。在信任系统下,提出了基于信任系统的有趣度度量标准,并量化了偏好的信任度和偏离度,用于描述用户偏好和信任系统的一致或相悖程度,并将用户偏好分为泛化偏好或个性化偏好,最终依据信任度和偏离度得出有趣度,从而找出最有趣的规则。在计算有趣度的过程中,提出了一个可以使用不同信任度公式来计算有趣度的可扩展的计算框架。为了进一步验证度量框架的准确性和有效性,以加权的余弦相似度公式和相关系数公式为例,提出了IMCos算法和IMCov算法。实验结果表明,信任度和偏离度有效地反映了偏好的不同特征,并且与两种最新的算法CONTENUM和TKO相比,度量框架发现的Top-K规则在召回率、准确率和F1-Measure等指标上均更优。

关键词: 共识偏好, 规则集聚合, 上下文偏好, 数据挖掘, 信任系统, 有趣度度量

Abstract: 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.

Key words: Belief system, Common preference, Contextual preference, Data mining, Interestingness measure, Ruleset aggregation

中图分类号:

TP311

余航, 魏炜, 谭征, 刘惊雷. 基于信任系统的条件偏好协同度量框架[J]. 计算机科学, 2020, 47(4): 74-84. https://doi.org/10.11896/jsjkx.190600152

YU Hang, WEI Wei, TAN Zheng, LIU Jing-lei. Contextual Preference Collaborative Measure Framework Based on Belief System[J]. Computer Science, 2020, 47(4): 74-84. https://doi.org/10.11896/jsjkx.190600152

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