Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
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发表时间：
2009-07
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通讯作者：
Bin Li;Qiang Yang;X. Xue
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作者：
Bin Li;Qiang Yang;X. Xue
协同过滤（CF）中的稀疏性问题是大多数CF方法的主要瓶颈。在本文中，我们考虑了一种新的方法，用于缓解CF中的稀疏性问题，通过将用户项评级模式从其他领域（例如，流行的电影评级网站）到目标域中的稀疏评级矩阵（例如，新书评级网站）。我们不要求两个域中的用户和项相同，甚至不要求它们重叠。基于目标矩阵中有限的评分，我们在用户-项目评分模式的聚类水平上建立了两个评分矩阵之间的桥梁，以便从辅助任务域中转移更多有用的知识。我们首先将辅助评级矩阵中的评级压缩成一个信息丰富且紧凑的聚类级评级模式表示，称为码本。然后，我们提出了一个有效的算法重建的目标评级矩阵，通过扩大码本。我们进行了广泛的实证测试，以表明与许多最先进的CF方法相比，我们的方法通过从辅助任务中转移有用的知识来有效地解决数据稀疏性问题。
The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.