{"title":"金融服务领域的不对称优势与妥协效应","authors":"E. Teppan, A. Felfernig","doi":"10.1109/CEC.2009.69","DOIUrl":null,"url":null,"abstract":"Recommender systems help customers toidentify products which fulfill certain requirements.Many different approaches have been developed andwork well for different domains. What has been ignoredso far is the fact that psychological phenomena can resultin suboptimal decision taking on recommender resultpages. This paper presents a user study investigating theAsymmetric Dominance Effect and the CompromiseEffect in the domain of financial services. The empiricalfindings are compared with the calculations of the SimpleDominance Model which serves as a model foridentification and explanation of such effects.Furthermore, we show how to identify misleading effectson recommender result pages, and how to correctproduct perception such that the felt utility reflects theutility calculated by recommender systems.","PeriodicalId":384060,"journal":{"name":"2009 IEEE Conference on Commerce and Enterprise Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Asymmetric Dominance- and Compromise Effects in the Financial Services Domain\",\"authors\":\"E. Teppan, A. Felfernig\",\"doi\":\"10.1109/CEC.2009.69\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recommender systems help customers toidentify products which fulfill certain requirements.Many different approaches have been developed andwork well for different domains. What has been ignoredso far is the fact that psychological phenomena can resultin suboptimal decision taking on recommender resultpages. This paper presents a user study investigating theAsymmetric Dominance Effect and the CompromiseEffect in the domain of financial services. The empiricalfindings are compared with the calculations of the SimpleDominance Model which serves as a model foridentification and explanation of such effects.Furthermore, we show how to identify misleading effectson recommender result pages, and how to correctproduct perception such that the felt utility reflects theutility calculated by recommender systems.\",\"PeriodicalId\":384060,\"journal\":{\"name\":\"2009 IEEE Conference on Commerce and Enterprise Computing\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Conference on Commerce and Enterprise Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2009.69\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Conference on Commerce and Enterprise Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2009.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Asymmetric Dominance- and Compromise Effects in the Financial Services Domain
Recommender systems help customers toidentify products which fulfill certain requirements.Many different approaches have been developed andwork well for different domains. What has been ignoredso far is the fact that psychological phenomena can resultin suboptimal decision taking on recommender resultpages. This paper presents a user study investigating theAsymmetric Dominance Effect and the CompromiseEffect in the domain of financial services. The empiricalfindings are compared with the calculations of the SimpleDominance Model which serves as a model foridentification and explanation of such effects.Furthermore, we show how to identify misleading effectson recommender result pages, and how to correctproduct perception such that the felt utility reflects theutility calculated by recommender systems.