{"title":"基于深度q学习的行为感知英语阅读文章推荐系统","authors":"Ting Zheng, Min Ding","doi":"10.4018/jcit.324102","DOIUrl":null,"url":null,"abstract":"Due to the differences of students' English proficiency and the rapid changes in reading interests, online personalized English reading recommendation is a highly challenging problem. Although some works have been proposed to address the dynamic change of recommendation, there are two issues with these methods. First, it only considers whether students have read the recommended articles. Second, these methods often fail to capture the real-time changing interests of users. To address the above challenges, a deep Q-network based recommendation framework was proposed. The authors further use the user's behavior and scores as reward information to get more user's feedback. In addition, a personalized adaptive module was introduced to capture the short-term interests on the fly and utilized the consistent loss of KL divergence to distill the knowledge from the online model. Extensive experiments on the offline and online dataset in the IWiLL website demonstrate the superior performance of the method.","PeriodicalId":43384,"journal":{"name":"Journal of Cases on Information Technology","volume":" ","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Behavior-Aware English Reading Article Recommendation System Using Online Distilled Deep Q-Learning\",\"authors\":\"Ting Zheng, Min Ding\",\"doi\":\"10.4018/jcit.324102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the differences of students' English proficiency and the rapid changes in reading interests, online personalized English reading recommendation is a highly challenging problem. Although some works have been proposed to address the dynamic change of recommendation, there are two issues with these methods. First, it only considers whether students have read the recommended articles. Second, these methods often fail to capture the real-time changing interests of users. To address the above challenges, a deep Q-network based recommendation framework was proposed. The authors further use the user's behavior and scores as reward information to get more user's feedback. In addition, a personalized adaptive module was introduced to capture the short-term interests on the fly and utilized the consistent loss of KL divergence to distill the knowledge from the online model. Extensive experiments on the offline and online dataset in the IWiLL website demonstrate the superior performance of the method.\",\"PeriodicalId\":43384,\"journal\":{\"name\":\"Journal of Cases on Information Technology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cases on Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/jcit.324102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cases on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/jcit.324102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Behavior-Aware English Reading Article Recommendation System Using Online Distilled Deep Q-Learning
Due to the differences of students' English proficiency and the rapid changes in reading interests, online personalized English reading recommendation is a highly challenging problem. Although some works have been proposed to address the dynamic change of recommendation, there are two issues with these methods. First, it only considers whether students have read the recommended articles. Second, these methods often fail to capture the real-time changing interests of users. To address the above challenges, a deep Q-network based recommendation framework was proposed. The authors further use the user's behavior and scores as reward information to get more user's feedback. In addition, a personalized adaptive module was introduced to capture the short-term interests on the fly and utilized the consistent loss of KL divergence to distill the knowledge from the online model. Extensive experiments on the offline and online dataset in the IWiLL website demonstrate the superior performance of the method.
期刊介绍:
JCIT documents comprehensive, real-life cases based on individual, organizational and societal experiences related to the utilization and management of information technology. Cases published in JCIT deal with a wide variety of organizations such as businesses, government organizations, educational institutions, libraries, non-profit organizations. Additionally, cases published in JCIT report not only successful utilization of IT applications, but also failures and mismanagement of IT resources and applications.