Implicit feedback recommender system based on matrix factorization

doi:10.4108/eai.28-9-2017.2273855

科研成果详情

题名	Implicit feedback recommender system based on matrix factorization
作者	Jiang，Na
发表日期	2018-01-08
会议录名称	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
ISSN	1867-8211
卷号	2017-September
摘要	With the development of the internet age, information overload problem is imminent. At now, almost of recommended models use the explicit feedback. But lots of implicit feedback data are missing. The paper explores the area of recommendation based on large-scale implicit feedback, Where only positive feedback is available. Further, the paper carried on the empirical research on the Implicit Feedback Recommendation Model. By maximized the probability of the user’s choices, IFR mean the progress task into optimization problems In the way, the experiment results confirm the superiority of the model. However, the model is insufficient about online research and a lack of details.
关键词	Implicit feedback Matrix decomposition Recommendation system
DOI	10.4108/eai.28-9-2017.2273855
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85051018192
引用统计
文献类型	会议论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/12870
专题	个人在本单位外知识产出
通讯作者	Jiang，Na
作者单位	Zhaotong University,China
推荐引用方式 GB/T 7714	Jiang，Na. Implicit feedback recommender system based on matrix factorization[C], 2018.