Research And Implementation Of Top-k Online Service Evaluation Based On Group Satisfaction

With the rapid development of the Internet,online services have gained rapid popularity and are widely used in Web services,e-commerce,e-government,and online learning.However,as the number of services continues to increase,users face multiple challenges when choosing satisfactory services.Firstly,the huge number of online services makes it impossible for users to interact with all services,and some users may be reluctant to evaluate all the services they interact with,witch make it difficult for users to obtain information about all online services.Then,some users or online service providers may provide false service information due to profit-driven.Therefore,it is important to study objective and effective online service evaluation methods to assist users in making service selection decisions.The Top-k online services evaluation scenario refers to the collective decision of user groups to select a specified number of service sets,and the evaluation results must fully reflect the satisfaction of each user.However,online services evaluation that considers inconsistent user evaluation criteria usually uses the complete ordering of services as the evaluation result,instead of selecting the Top-k service set that maximizes the user group satisfaction,witch makes it difficult for the evaluation results to meet the need of rationality and fairness for Top-k online service evaluation scenarios.In addition,because the users' evaluation criteria for different services are different,the users' evaluation information is not comparable,which makes the results of the evaluation method assuming that users have the same evaluation criterion lack a certain rationality.Considering that the existing research is not applicable to the Top-k online services evaluation scenario and the problem that user evaluation information is not comparable due to inconsistent user evaluation criteria,this paper proposes a Top-k online services evaluation method that maximizes user group satisfaction.Firstly,a metric of user group satisfaction is defined to measure the rationality of the selected k online services.Secondly,considering the inconsistency of user evaluation criteria and incomplete user preference information,the Borda rule is used to construct user-service matrix based on users' preference relationship for online services.Then,inspired by the theory of Monroe proportional representation,the Top-k online services evaluation problem is modeled as an optimization problem to find a set of online services that maximizes satisfaction of the user group.Finally,a greedy algorithm is designed to solve the optimization problem and the obtained set of online services is served as the result of Top-k services evaluation.The rationality and effectiveness of the method are verified by theoretical analysis and experiments study.Theoretical analysis shows that the method satisfies the proportional representation and fairness required for Top-k online services evaluation.Meanwhile,experiments also show that the method can obtain the result close to the ideal upper bound of the user group satisfaction in the reasonable time,so that the user group can make right services choice decision.In addition,the method can also realize Top-k online services evaluation when users' preferences are incomplete.