Transfer learning for collaborative filtering via a rating-matrix generative model
Transfer learning for collaborative filtering via a rating-matrix generative model
复制标题
DOI：
10.1145/1553374.1553454
复制
发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Bin Li;Qiang Yang;X. Xue
中科院分区：
文献类型：
--
作者：
Bin Li;Qiang Yang;X. Xue
跨域协同过滤通过在多个域之间传递评级知识来解决稀疏性问题。本文提出了一种用于有效的跨域协同过滤的评分矩阵生成模型。我们首先证明了多个评级矩阵之间的相关性可以通过找到一个共享的隐式簇级评级矩阵来建立，然后将其扩展到一个簇级评级模型。因此，任何相关任务的评级矩阵可以被视为从用户-项目联合混合模型中提取一组用户和项目，以及从簇级评级模型中提取相应的评级。这两种模式的结合产生了RMGM，它可以用来填补现有用户和新用户的评级缺失。RMGM的一个主要优势是，即使这些任务的用户和项目不重叠，它也可以通过汇集来自多个任务的评级数据来共享知识。我们在三个真实世界的协同过滤数据集上对RMGM进行了实证评估，结果表明RMGM的性能优于单独训练的单个模型。
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.