G. Leonardi, L. Portinale, P. Artusio, Marco Valsania
{"title":"Recommending Personalized Asset Investments through Case-Based Reasoning: The SMARTFASI System","authors":"G. Leonardi, L. Portinale, P. Artusio, Marco Valsania","doi":"10.1109/ICTAI.2016.0126","DOIUrl":null,"url":null,"abstract":"Personalized financial advisory systems based on Case-Based Reasoning and on historical user activity are an emerging trend. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SMARTFASI, where the knowledge about past experiences is exploited, in order to suggest suitable asset investments to the final user. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile \"similar\" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SMARTFASI. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users.","PeriodicalId":245697,"journal":{"name":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2016.0126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Personalized financial advisory systems based on Case-Based Reasoning and on historical user activity are an emerging trend. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SMARTFASI, where the knowledge about past experiences is exploited, in order to suggest suitable asset investments to the final user. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile "similar" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SMARTFASI. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users.