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通过评级矩阵生成模型进行协同过滤的转移学习
Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model |
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| 课程网址: |
http://videolectures.net/icml09_li_tlcfvrmgm/
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| 主讲教师: |
Bin Li
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| 开课单位: |
复旦大学
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| 开课时间: |
2009-08-26
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| 课程语种: |
汉简
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| 中文简介: |
跨域协作过滤通过跨多个域转移评级知识来解决稀疏性问题。在本文中，我们提出了一个评级矩阵生成模型（RMGM），用于有效的跨域协作过滤。我们首先表明，可以通过找到共享的隐式集群级别评级矩阵来建立跨多个评级矩阵的相关性，该矩阵接下来扩展到集群级别评级模型。因此，任何相关任务的评级矩阵可被视为从用户项目联合混合模型中绘制一组用户和项目，以及从集群级别评级模型中绘制相应的评级。这两种模型的组合提供了RMGM，可用于填写现有用户和新用户的缺失评级。 RMGM的一个主要优点是，即使用户和这些任务的项目不重叠，它也可以通过汇集来自多个任务的评级数据来共享知识。我们根据三个真实世界的协作过滤数据集对RMGM进行经验评估，以表明RMGM可以胜过单独训练的单个模型。
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| 课程简介: |
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.
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| 关 键 词: |
跨域协作; 稀疏性问题; 评级矩阵
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-23：lxf
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| 阅读次数: |
160
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