An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems
文献类型：期刊论文
| 作者 | Wu, Di2,3,4 ; Shang, Mingsheng2,3; Luo, Xin1,2,3,4; Wang, Zidong5
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2021-04-22 |
| 页码 | 14 |
| ISSN号 | 2162-237X |
| 关键词 | High-dimensional and sparse (HiDS) matrix latent factor (LF) analysis L-1 norm L-2 norm recommender system (RS) |
| DOI | 10.1109/TNNLS.2021.3071392 |
| 通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
| 英文摘要 | A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L-2 norm-oriented one, which ignores target data's characteristics described by other metrics like an L-1 norm-oriented one. To investigate this issue, this article proposes an L-1-and-L-2-norm-oriented LF((LF)-F-3) model. It adopts twofold ideas: 1) aggregating L-1 norm's robustness and L-2 norm's stability to form its Loss and 2) adaptively adjusting weights of L-1 and L-2 norms in its Loss. By doing so, it achieves fine aggregation effects with L-1 norm-oriented Loss's robustness and L-2 norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an (LF)-F-3 model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications. |
| 资助项目 | National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[62002337] ; National Natural Science Foundation of China[61902370] ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ2020-004B] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyj-msxmX0578] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Chinese Academy of Sciences Light of West China Program ; Technology Innovation and Application Development Project of Chongqing, China[cstc2018jszx-cyzdX0041] ; Technology Innovation and Application Development Project of Chongqing, China[cstc2019jscx-fxydX0027] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000732909100001 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15084]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Luo, Xin |
| 作者单位 | 1.Cloudwalk, Dept Big Data Anal Tech, Chongqing 401331, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 4.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China 5.Brunel Univ, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England |
| 推荐引用方式 GB/T 7714 | Wu, Di,Shang, Mingsheng,Luo, Xin,et al. An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:14. |
| APA | Wu, Di,Shang, Mingsheng,Luo, Xin,&Wang, Zidong.(2021).An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14. |
| MLA | Wu, Di,et al."An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):14. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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