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Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Transfer Learning in Collaborative Filtering for Sparsity Reduction
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Nathan N. Yang, Qiang Xiang, Evan W. Pan, Weike |
| Abstract | 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Data Sparsity User Feedback Collaborative Filtering Sparsity Reduction Twenty-fourth Aaai Conference Target Data Coordinate System Transfer Different Domain Item Knowledge Data Sparsity Problem Cf System New User Data Heterogeneity Principle Coordinate Target Domain Principled Matrix-based Transfer Dense Auxiliary Data Auxiliary Data Matrix Several State-of-the-art Solution Rating V Recommender System Auxiliary Data Source Mature Application Domain Major Problem |
| Content Type | Text |
