{"title":"用户信息访问行为估计的逐步适应模型","authors":"Jing Chen, Roman Y. Shtykh, Qun Jin","doi":"10.1109/ICSNC.2008.56","DOIUrl":null,"url":null,"abstract":"In this study, we propose a gradual adaption model for estimation of user information access behavior. A variety of userspsila information access data are collected by unit of a day for each user, and analyzed in terms of short, medium, long periods, and by remarkable and exceptional categories. The proposed model is then established by analyzing the pre-processed data based on Full Bayesian Estimation. We further present experimental simulation results, and show the operability and effectiveness of our proposed model.","PeriodicalId":105399,"journal":{"name":"2008 Third International Conference on Systems and Networks Communications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Gradual Adaption Model for Estimation of User Information Access Behavior\",\"authors\":\"Jing Chen, Roman Y. Shtykh, Qun Jin\",\"doi\":\"10.1109/ICSNC.2008.56\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we propose a gradual adaption model for estimation of user information access behavior. A variety of userspsila information access data are collected by unit of a day for each user, and analyzed in terms of short, medium, long periods, and by remarkable and exceptional categories. The proposed model is then established by analyzing the pre-processed data based on Full Bayesian Estimation. We further present experimental simulation results, and show the operability and effectiveness of our proposed model.\",\"PeriodicalId\":105399,\"journal\":{\"name\":\"2008 Third International Conference on Systems and Networks Communications\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Third International Conference on Systems and Networks Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSNC.2008.56\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Third International Conference on Systems and Networks Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSNC.2008.56","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gradual Adaption Model for Estimation of User Information Access Behavior
In this study, we propose a gradual adaption model for estimation of user information access behavior. A variety of userspsila information access data are collected by unit of a day for each user, and analyzed in terms of short, medium, long periods, and by remarkable and exceptional categories. The proposed model is then established by analyzing the pre-processed data based on Full Bayesian Estimation. We further present experimental simulation results, and show the operability and effectiveness of our proposed model.