基于熵值尺度的认知混合协同过滤防御先令影响

Deyu Deng, Jiaxing Jason Mai, C. Leung, A. Cuzzocrea
{"title":"基于熵值尺度的认知混合协同过滤防御先令影响","authors":"Deyu Deng, Jiaxing Jason Mai, C. Leung, A. Cuzzocrea","doi":"10.1145/3375998.3376040","DOIUrl":null,"url":null,"abstract":"In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence\",\"authors\":\"Deyu Deng, Jiaxing Jason Mai, C. Leung, A. Cuzzocrea\",\"doi\":\"10.1145/3375998.3376040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.\",\"PeriodicalId\":395773,\"journal\":{\"name\":\"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3375998.3376040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3375998.3376040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

在当前的大数据时代,大量、种类繁多的有价值数据被高速生成和收集。因此,需要数据科学解决方案对这些大数据进行数据挖掘,以获得嵌入在这些大数据中的有价值的信息和有用的知识,并将这些信息和知识转化为建议和行动。特别是推荐系统(RecSys或RS),它是一种可以根据各种指标向用户提供建议的工具,自互联网蓬勃发展以来,它一直在社会中发挥着重要作用。做出更准确的预测既可以潜在地增加公司收入,又可以增强用户体验。所以,这一直是一个热门话题。更具体地说,协同过滤(CF)是RS中应用的一种流行技术,大多数CF算法背后的关键思想是根据其他用户的意见过滤项目。由于推荐过程是基于用户交互的,因此其中一个挑战是如何防止先令攻击(或先令攻击评级)。在本文中,我们提出了将用户评分熵融入协同过滤的方法,以防御先令攻击和降低噪声评分,从而达到更高的预测精度。评估结果表明,基于认知的混合协同过滤方法在防御先令影响的熵评级缩放方面是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence
In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.
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
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信