基于不对称相似度与平均组满意度的需求群组融合  

Requirement Group Fusion Based on Asymmetric Similarity and Average Group Satisfaction

作　　者：李志鹏 刘茜萍[1] 张琳[1] LI Zhipeng;LIU Xiping;ZHANG Lin(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023

出　　处：《软件导刊》2024年第12期112-118,共7页Software Guide

摘　　要：随着服务计算技术的蓬勃发展,服务范畴已从线上服务扩展到线下旅游、购物、餐饮等各行业领域,产生了海量的个性化服务定制需求。然而,鉴于定制成本等因素,服务提供商往往不会为小规模用户逐一提供个性化定制服务。从大量用户的个性化服务定制需求中找到共性,将相似需求聚类融合成组,以形成较大规模的群组定制需求将有望建立供需双赢局面。这一需求成组操作需基于需求之间的不对称相似度开展,而现有的聚类算法都是依靠相似度进行的,并没有考虑聚类后对象的兼容性。为此,提出针对个人服务定制需求的群组融合方法,增加了满意度这一限制条件,在此条件下进行需求对象的聚类工作,并在建立定制需求模型的基础上给出需求之间不对称相似度的计算方法,进而以最大平均组满意度为优化目标设计群组构建及融合算法,以将若干相似的个人定制需求成组并融合为一个全组满意的群组定制需求。通过具体实验演示,证明了该方法的可行性和有效性。With the booming development of service computing technology,the scope of services has expanded from online services to offline travel,shopping,catering and other industry sectors,generating massive demand for personalized service customization.However,given the cost of customization and other factors,service providers often do not provide personalized services for small-scale users one by one.Finding commonalities in the personalized service customization needs of a large number of users,and clustering and fusing similar needs into groups to form larger-scale group customization needs are expected to establish a win-win situation for both supply and demand.This demand grouping operation needs to be carried out based on the asymmetric similarity between demands,while existing clustering algorithms rely on similarity and do not consider the compatibility of objects after clustering.To this end,a group fusion method is proposed for personal service customization requirements,with the added constraint of satisfaction,under which the clustering of demand objects is carried out,and the calculation method of asymmetric similarity between requirements is given based on the establishment of the customization requirement model,and then the group construction and fusion algorithms are designed with the optimization objective of maximum average group satisfaction,in order to group several similar personal customization requirements into a single group and to fused into a group customization requirement with full group satisfaction,and finally demonstrated the feasibility and effectiveness of the method through specific experiments.

关 键 词：服务计算 个性化定制 不对称相似度 群组融合 聚类 组满意度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]