{"title":"人工免疫系统电影推荐的亲和度量研究","authors":"U. Aickelin, Qi Chen","doi":"10.2139/ssrn.2832023","DOIUrl":null,"url":null,"abstract":"We combine Artificial Immune Systems 'AIS', technology with Collaborative Filtering 'CF' and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin 3, 4, 5. Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendalls Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.","PeriodicalId":437878,"journal":{"name":"IRPN: Technology Transfer (Topic)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"On Affinity Measures for Artificial Immune System Movie Recommenders\",\"authors\":\"U. Aickelin, Qi Chen\",\"doi\":\"10.2139/ssrn.2832023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We combine Artificial Immune Systems 'AIS', technology with Collaborative Filtering 'CF' and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin 3, 4, 5. Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendalls Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.\",\"PeriodicalId\":437878,\"journal\":{\"name\":\"IRPN: Technology Transfer (Topic)\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IRPN: Technology Transfer (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2832023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IRPN: Technology Transfer (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2832023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On Affinity Measures for Artificial Immune System Movie Recommenders
We combine Artificial Immune Systems 'AIS', technology with Collaborative Filtering 'CF' and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin 3, 4, 5. Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendalls Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.