图 书 馆
离散的潜在因素模型预测大型二进数据Predictive Discrete Latent Factor Models for Large Scale Dyadic Data |
|
| 课程网址: | http://videolectures.net/kdd07_agarwal_pdlfm/ |
| 主讲教师: | Deepak Agarwal |
| 开课单位: | 领英公司 |
| 开课时间: | 2007-08-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a novel statistical method to predict large scale dyadic response variables in the presence of covariate information. Our approach simultaneously incorporates the effect of covariates and estimates local structure that is induced by interactions among the dyads through a discrete latent factor model. The discovered latent factors provide a predictive model that is both accurate and interpretable. We illustrate our method by working in a framework of generalized linear models, which include commonly used regression techniques like linear regression, logistic regression and Poisson regression as special cases. We also provide scalable generalized EM-based algorithms for model fitting using both "hard" and "soft" cluster assignments. We demonstrate the generality and efficacy of our approach through large scale simulation studies and analysis of datasets obtained from certain real-world movie recommendation and internet advertising applications. |
| 关 键 词: | 二元响应变量; 协变量; 估计的局部结构; 广义线性模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-31：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 67 |