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跟踪规范的协作过滤:学习、边界和转换Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing |
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| 课程网址: | http://videolectures.net/colt2011_shamir_filtering/ |
| 主讲教师: | Ohad Shamir |
| 开课单位: | 魏茨曼科学研究所 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Trace-norm regularization is a widely-used and successful approach for collaborative filtering and matrix completion. However, its theoretical understanding is surprisingly weak, and despite previous attempts, there are no distribution-free, non-trivial learning guarantees currently known. In this paper, we bridge this gap by providing such guarantees, under mild assumptions which correspond to collaborative fi ltering as performed in practice. In fact, we claim that previous difficulties partially stemmed from a mismatch between the standard learning-theoretic modeling of collaborative filtering, and its practical application. Our results also shed some light on the issue of collaborative filtering with bounded models, which enforce predictions to lie within a certain range. In particular, we provide experimental and theoretical evidence that such models lead to a modest yet signifi cant improvement. |
| 关 键 词: | 跟踪-范数正则化; 学习理论模型; 实际应用 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2019-05-26：cwx |
| 阅读次数: | 86 |