|
Top-N推荐的局部潜在空间模型
Local Latent Space Models for Top-N Recommendation |
|
| |
| 课程网址: |
http://videolectures.net/kdd2018_christakopoulou_space_models/
|
| 主讲教师: |
Evangelia Christakopoulou
|
| 开课单位: |
明尼苏达大学
|
| 开课时间: |
2018-11-23
|
| 课程语种: |
英语
|
| 中文简介: |
用户的行为是由他们对购买、观看等潜在感兴趣的物品的各个方面的偏好所驱动的。潜在空间方法以潜在因素的形式对这些方面进行建模。尽管这种方法已被证明会产生良好的结果，但对不同用户来说重要的方面可能有所不同。在许多领域中，可能存在一组所有用户都关心的方面，以及一组特定于不同用户子集的方面。为了明确地捕捉这一点，我们考虑这样的模型：其中有一些潜在因素捕捉共享方面，而一些用户子集特定的潜在因素捕捉不同用户子集所关心的一组方面。
特别是，我们提出了两个潜在空间模型：rGLSVD和sGLSVD，它们结合了这样一组全局和用户子集特定的潜在因素。rGLSVD模型基于用户的评级模式将用户分配到不同的子集中，然后估计潜在维度数量可以变化的全局和一组用户子集特定的局部模型。
sGLSVD模型通过保持这些模型中潜在维度的数量相同来估计全局和用户子集特定的局部模型，但优化用户的分组以实现最佳近似。我们在各种真实世界数据集上的实验表明，所提出的方法显著优于最先进的潜在空间top-N推荐方法。
|
| 课程简介: |
Users’ behaviors are driven by their preferences across various aspects of items they are potentially interested in purchasing, viewing, etc. Latent space approaches model these aspects in the form of latent factors. Although such approaches have been shown to lead to good results, the aspects that are important to different users can vary. In many domains, there may be a set of aspects for which all users care about and a set of aspects that are specific to different subsets of users. To explicitly capture this, we consider models in which there are some latent factors that capture the shared aspects and some user subset specific latent factors that capture the set of aspects that the different subsets of users care about.
In particular, we propose two latent space models: rGLSVD and sGLSVD, that combine such a global and user subset specific sets of latent factors. The rGLSVD model assigns the users into different subsets based on their rating patterns and then estimates a global and a set of user subset specific local models whose number of latent dimensions can vary.
The sGLSVD model estimates both global and user subset specific local models by keeping the number of latent dimensions the same among these models but optimizes the grouping of the users in order to achieve the best approximation. Our experiments on various real-world datasets show that the proposed approaches significantly outperform state-of-the-art latent space top-N recommendation approaches.
|
| 关 键 词: |
Top-N推荐; 局部潜在空间模型; 真实世界数据集; rGLSVD和sGLSVD; 潜在因素捕捉共享方面
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2023-03-08：cyh
|
| 最后编审: |
2023-03-08：cyh
|
| 阅读次数: |
52
|
|
图 书 馆