Long-term performance of collaborative filtering based recommenders in temporally evolving systems
文献类型：期刊论文
| 作者 | Shi, Xiaoyu1 ; Luo, Xin1,2; Shang, Mingsheng1; Gu, Liang3,4
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2017-12-06 |
| 卷号 | 267页码:635-643 |
| 关键词 | Learning system Recommender system One-step recommendation Long-term effect Temporally evolving system Bipartite network |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2017.06.026 |
| 通讯作者 | Luo, X (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China. |
| 英文摘要 | Recommender systems benefit people at every moment in their daily life. Considerable attentions have been drawn by performance in one-step recommendation and static user-item network, while the performances of recommenders on temporally evolving systems remain unclear. To address this issue, this paper first describes an online commercial system by using a bipartite network. On this network, a recommendation-based evolution method is proposed to simulate the temporal dynamics between a recommender and its users. Then the long-term performance of three state-of-the-art collaborative filtering (CF)-based recommenders, i.e., the user-based CF (UCF), item-based CF (ICF) and latent factor based model (LFM), is evaluated on the generated temporally evolving networks. Experimental results on two large, real datasets generated by industrial applications demonstrate that 1) optimization-based CF models like the LFM enjoy their high-prediction accuracy in one-step recommendation; and 2) entity relationship-based CF models like the ICF benefit the recommendation diversity, as well as the system health on a temporally evolving network. It turns out that in a temporally evolving system, an efficient recommender should consider both the one-step and long-term effects to generate satisfactory recommendations. Thus, it is necessary to adopt heterogeneous models, e.g., trade-off between optimization based model and entity relationship-based model, in real systems to grasp various users' behavior patterns to improve their experiences. (c) 2017 Elsevier B.V. All rights reserved. |
| 资助项目 | Chinese Academy of Sciences ; Royal Society of U.K. ; National Natural Science Foundation of China[61611130209] ; National Natural Science Foundation of China[61402198] ; National Natural Science Foundation of China[61602434] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61672136] ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] ; Chongqing Research Program of Basic Research and Frontier Technology[cstc2015jcyjB0244] ; Youth Innovation Promotion Association CAS[2017393] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000409285400058 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://172.16.51.4:88/handle/2HOD01W0/104]  |
| 专题 | 大数据挖掘及应用中心 |
| 通讯作者 | Luo, Xin |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.Shenzhen Univ, Coll Comp Sci & Engn, Shenzhen 518060, Peoples R China 3.Jinan Univ, Guangzhou 510632, Guangdong, Peoples R China 4.Sangfor Technol Inc, Shenzhen 518057, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Xiaoyu,Luo, Xin,Shang, Mingsheng,et al. Long-term performance of collaborative filtering based recommenders in temporally evolving systems[J]. NEUROCOMPUTING,2017,267:635-643. |
| APA | Shi, Xiaoyu,Luo, Xin,Shang, Mingsheng,&Gu, Liang.(2017).Long-term performance of collaborative filtering based recommenders in temporally evolving systems.NEUROCOMPUTING,267,635-643. |
| MLA | Shi, Xiaoyu,et al."Long-term performance of collaborative filtering based recommenders in temporally evolving systems".NEUROCOMPUTING 267(2017):635-643. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。