|
|||||
|
|
| 特征选择在协同过滤推荐算法中的应用研究 | |
| 引用本文： | 李晓艳,周兴弛,殷海娜.特征选择在协同过滤推荐算法中的应用研究[J].价值工程,2012,31(17):182-184. |
| 作者姓名： | 李晓艳 周兴弛 殷海娜 |
| 作者单位： | 1. 华中科技大学管理学院,武汉,430074 2. 重庆大学经济与工商管理学院,重庆,400030 3. 吉林大学珠海学院,珠海,519041 |
| 摘 要： | 用户-项目评分数据集的高维稀疏性使得传统的协同过滤处于"维度困境"。运用降维技术的特征变换方法的协同过滤算法虽然缩减用户-项目评分数据集规模,但在某种程度上导致信息损失。本文提出将特征选择方法和技术运用于协同过滤算法,并且给出了基于有监督特征选择的协同过滤框架及其协同过滤流程。 |
| 关 键 词： | 特征选择 协同过滤算法 稀疏性 |
The Research on Feature Selection Applied in the Collaborative Filtering Algorithm |
|
| Li Xiaoyan , Zhou Xingchi , Yin Haina.The Research on Feature Selection Applied in the Collaborative Filtering Algorithm[J].Value Engineering,2012,31(17):182-184. | |
| Authors: | Li Xiaoyan Zhou Xingchi Yin Haina |
| Institution: | Li Xiaoyan; Zhou Xingchi; Yin Haina(①School of Management,Huazhong University of Science and Technology,Wuhan 430074,China;② Economics and Business Administration,Chongqing University,Chongqing 400030,China;③Zhuhai College of Jilin University,Zhuhai 519041,China) |
| Abstract: | High-dimensional sparisity of user-item rating data sets made traditional collaborative filtering in dimension dilemma.Although feature transform technology applied in the collaborative filtering algorithm reduces the scale of user-item rating data sets,it leads to information loss.The paper proposes that feature selection methods and techniques are used in collaborative filtering algorithm,and gives the collaborative filtering framework and collaborative filtering process based on supervised feature selection. |
| Keywords: | feature selection collaborative filtering algorithm sparsity |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |