基于深度q学习的行为感知英语阅读文章推荐系统

IF 0.7 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ting Zheng, Min Ding
{"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}
引用次数: 0

摘要

由于学生英语水平的差异和阅读兴趣的快速变化,在线个性化英语阅读推荐是一个极具挑战性的问题。虽然已经提出了一些工作来解决推荐的动态变化,但这些方法存在两个问题。首先,它只考虑学生是否阅读了推荐的文章。其次,这些方法往往无法捕捉到用户兴趣的实时变化。为了解决上述挑战,提出了一种基于深度q网络的推荐框架。作者进一步使用用户的行为和分数作为奖励信息,以获得更多的用户反馈。此外,引入个性化自适应模块捕捉动态的短期利益,并利用KL散度的一致损失从在线模型中提取知识。在IWiLL网站的离线和在线数据集上进行的大量实验证明了该方法的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Cases on Information Technology
Journal of Cases on Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.60
自引率
0.00%
发文量
64
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信