{"title":"具有区间值偏好序信息的多属性严格双边匹配方法","authors":"Decui Liang, Xin He, Zeshui Xu, Jiahong Li","doi":"10.1080/0952813X.2021.1907794","DOIUrl":null,"url":null,"abstract":"ABSTRACT In the study of two-sided matching decision problems, preference ordinal information is a key factor. However, in real life, it is often difficult to ascertain complete preference ordinal information, and in most cases we can only obtain an interval-valued preference ordinal information. In this paper, a strict two-sided matching based on multi-attribute interval-valued preference ordinal information is discussed. As a generalised decision model, the strict two-sided matching adequately considers the requirement of satisfaction degree of two-sided agents. Firstly, the ranking method of probability degree is introduced to deal with the information of various interval numbers. Then, in the case of multiple attributes, we propose two methods for strict two-sided matching problem. The one is to aggregate multi-attribute satisfaction degree and then construct the decision model. The another is to separately deal with the interval-valued preference ordinal information of each attribute and then design the corresponding model. Finally, in the context of Internet finance, we adopt an example of the venture capital two-sided matching problem to illustrate our proposed methods and confirm the effectiveness.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"40 1","pages":"545 - 569"},"PeriodicalIF":1.7000,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-attribute strict two-sided matching methods with interval-valued preference ordinal information\",\"authors\":\"Decui Liang, Xin He, Zeshui Xu, Jiahong Li\",\"doi\":\"10.1080/0952813X.2021.1907794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT In the study of two-sided matching decision problems, preference ordinal information is a key factor. However, in real life, it is often difficult to ascertain complete preference ordinal information, and in most cases we can only obtain an interval-valued preference ordinal information. In this paper, a strict two-sided matching based on multi-attribute interval-valued preference ordinal information is discussed. As a generalised decision model, the strict two-sided matching adequately considers the requirement of satisfaction degree of two-sided agents. Firstly, the ranking method of probability degree is introduced to deal with the information of various interval numbers. Then, in the case of multiple attributes, we propose two methods for strict two-sided matching problem. The one is to aggregate multi-attribute satisfaction degree and then construct the decision model. The another is to separately deal with the interval-valued preference ordinal information of each attribute and then design the corresponding model. Finally, in the context of Internet finance, we adopt an example of the venture capital two-sided matching problem to illustrate our proposed methods and confirm the effectiveness.\",\"PeriodicalId\":15677,\"journal\":{\"name\":\"Journal of Experimental & Theoretical Artificial Intelligence\",\"volume\":\"40 1\",\"pages\":\"545 - 569\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2021-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental & Theoretical Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/0952813X.2021.1907794\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental & Theoretical Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/0952813X.2021.1907794","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Multi-attribute strict two-sided matching methods with interval-valued preference ordinal information
ABSTRACT In the study of two-sided matching decision problems, preference ordinal information is a key factor. However, in real life, it is often difficult to ascertain complete preference ordinal information, and in most cases we can only obtain an interval-valued preference ordinal information. In this paper, a strict two-sided matching based on multi-attribute interval-valued preference ordinal information is discussed. As a generalised decision model, the strict two-sided matching adequately considers the requirement of satisfaction degree of two-sided agents. Firstly, the ranking method of probability degree is introduced to deal with the information of various interval numbers. Then, in the case of multiple attributes, we propose two methods for strict two-sided matching problem. The one is to aggregate multi-attribute satisfaction degree and then construct the decision model. The another is to separately deal with the interval-valued preference ordinal information of each attribute and then design the corresponding model. Finally, in the context of Internet finance, we adopt an example of the venture capital two-sided matching problem to illustrate our proposed methods and confirm the effectiveness.
期刊介绍:
Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research.
The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following:
• cognitive science
• games
• learning
• knowledge representation
• memory and neural system modelling
• perception
• problem-solving