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| The existing cross domain recommendation algorithm can solve the problem of sparse data in the target domain by transferring the knowledge from the auxiliary domain. The key problem is how to map the user feature in different domains. In traditional transfer learning algorithms, user's feature vector is mapped to the target domain in a linear way, but the limitation of this method is that the real data does not always follow the linear mapping relations. In our previous work, we utilize support vector regression as the nonlinear function in mapping user feature across different domains, and demonstrate its effectiveness in improving the recommendation performance for new users. However, in the previous proposed framework, the mapping functions for different dimentions in a user feature vector are learned independently. Consequently, the optimization objective is an indirect one, and cannot reflect the error of rating prediction. In this paper, we propose a novel model that extends the previous model from independently modeling the mapping functions to jointly modeling the mapping functions. We utilize the rating prediction error as a bridge for learning different mapping functions. Through the experimental analysis, it is proved that the proposed method consistantly outperforms previous ones. |
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| Keywords:Recommended System;Transfer Learning;Collaborative Filtering |
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