图 书 馆
推荐系统中数据组合的概率模型Probabilistic Models for Data Combination in Recommender Systems |
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| 课程网址: | http://videolectures.net/lms08_williamson_pmdc/ |
| 主讲教师: | Sinead Williamson |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2008-12-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a method for jointly learning multiple related matrices, and show that, by sharing information between the two matrices, such an approach allows us to improve predictive performances for items where one of the matrices contains very sparse, or no, information. While the above justification has focused on recommender systems, the approach described is applicable to any two datasets that relate to a common set of items and can be represented in matrix form. Examples of such problems could include image data where each image is associated with a set of words (for example captioned or tagged images); sets of scientific papers that can be represented either using a bag-of-words representation or in terms of their citation links to and from other papers; corpora of documents that exist in two languages. |
| 关 键 词: | 联合学习多个相关矩阵; 推荐系统; 数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：yumf |
| 阅读次数: | 37 |