本文要解决的问题：First, we have to decide how to integrate the heterogeneous forms of user feedbacks, which are explicit ratings in the target domain and implicit feedbacks in the auxiliary domain. Second, we must incorporate both the user and item knowledge from the auxiliary domain in a flexible way.
本文做法核心思想：找两个source domains，分别进行SVD分解，一个domain得到U0，一个domain得到V0，而后以U0和V0作为所谓的principle coordinates去约束target domain中矩阵分解的U和V，约束方法最小化U和U0，V和V0之间的距离。
个人评价：SVD分解不唯一，所以U0和V0存在随机性，另外U0和U以及V0和V之间并不存在数学上的联系，仅仅是假设有联系（其实是做了一个莫名的约束，这个约束并没有什么道理？），并不合理和严谨。不过从聚类的角度，似乎说得通。（Transfer 聚类关系），另外从Transfer近邻关系的角度也能说的通
本文对比实验也很弱：平均值；LFM，CMF，OptSpace
SVD ref： http://www.cnblogs.com/zephyr-1/p/5809993.html ， http://www.cnblogs.com/LeftNotEasy/archive/2011/01/19/svd-and-applications.html
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Transfer Learning in Collaborative Filtering for Sparsity Reduction | |||||||||||
| 文献作者 | Weike Pan, Evan W. Xiang, Nathan N. Liu and Qiang Yang | ||||||||||
| 文献发表年限 | 2010 | ||||||||||
| 文献关键字 | |||||||||||
| cross domain; learn and transfer the latent vectors; principled matrix; heterogeneous； intrinsic preference structure; Y_{u:}行,Y_{:i}列，y_{ui}实体; 将ratings 处理成implicit feedback； coordinate systems | |||||||||||
| 摘要描述 | |||||||||||
| Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem. | |||||||||||
