图 书 馆
改进最大边缘矩阵分解Improving Maximum Margin Matrix Factorization |
|
| 课程网址: | http://videolectures.net/ecmlpkdd08_karatzoglou_immm/ |
| 主讲教师: | Alexandros Karatzoglou; Alexander J. Smola; Markus Weimer |
| 开课单位: | 微软公司 |
| 开课时间: | 2008-10-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov. Experimental evaluation of the introduced extensions showimproved performance over the original MMMF formulation. |
| 关 键 词: | 计算机科学; 机器学习; 推荐系统 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-04-13：chenxin |
| 阅读次数: | 63 |