Requirement Group Fusion Based on Asymmetric Similarity and Average Group Satisfaction
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 group-ing operation needs to be carried out based on the asymmetric similarity between demands,while existing clustering algorithms rely on similari-ty and do not consider the compatibility of objects after clustering.To this end,a group fusion method is proposed for personal service custom-ization requirements,with the added constraint of satisfaction,under which the clustering of demand objects is carried out,and the calcula-tion 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.
service calculationpersonalizationasymmetric similaritygroup fusionclusteringgroup satisfaction
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