{"title":"可信云服务选择的协同过滤方法","authors":"Guojun Sheng, Y. Cao, Yanxia Lu, Yao Li","doi":"10.1109/ICISCE.2016.14","DOIUrl":null,"url":null,"abstract":"This paper investigates the problem of trustworthy cloud service selection based on collaborative filtering in open network environment. First, it searches a set of Cloud services with same or similar functions according to the user's requirement, then computes the collection of users that have similar preferences for the target user based on their common evaluations, and obtains the recommendable user set for each candidate service, computes the recommendation degree for each service using it's similarity, and sorts the candidate services according to the recommendation degree to the target user. The experimental results show that the referral effect increases with the increase in the number of user evaluations, and with the increase of malicious user evaluations in the system, compared to other service recommendation methods, it only has a slight impact on the referral result of this method.","PeriodicalId":6882,"journal":{"name":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","volume":"14 1","pages":"13-16"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Collaborative Filtering Method for Trustworthy Cloud Service Selection\",\"authors\":\"Guojun Sheng, Y. Cao, Yanxia Lu, Yao Li\",\"doi\":\"10.1109/ICISCE.2016.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the problem of trustworthy cloud service selection based on collaborative filtering in open network environment. First, it searches a set of Cloud services with same or similar functions according to the user's requirement, then computes the collection of users that have similar preferences for the target user based on their common evaluations, and obtains the recommendable user set for each candidate service, computes the recommendation degree for each service using it's similarity, and sorts the candidate services according to the recommendation degree to the target user. The experimental results show that the referral effect increases with the increase in the number of user evaluations, and with the increase of malicious user evaluations in the system, compared to other service recommendation methods, it only has a slight impact on the referral result of this method.\",\"PeriodicalId\":6882,\"journal\":{\"name\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"volume\":\"14 1\",\"pages\":\"13-16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2016.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2016.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Collaborative Filtering Method for Trustworthy Cloud Service Selection
This paper investigates the problem of trustworthy cloud service selection based on collaborative filtering in open network environment. First, it searches a set of Cloud services with same or similar functions according to the user's requirement, then computes the collection of users that have similar preferences for the target user based on their common evaluations, and obtains the recommendable user set for each candidate service, computes the recommendation degree for each service using it's similarity, and sorts the candidate services according to the recommendation degree to the target user. The experimental results show that the referral effect increases with the increase in the number of user evaluations, and with the increase of malicious user evaluations in the system, compared to other service recommendation methods, it only has a slight impact on the referral result of this method.