关键词: 迁移学习, 推荐方法, 域自适应, 数据稀疏, 特征映射

Abstract: Collaborative filtering recommendation method performance decreases, when the target rating data is very sparse. The cross domain recommendation method can solve the problem of data sparsity to a certain extent, but for heterogeneous data in different domains, it may lead to negative transfer if no feature mapping processing is performed. Adopting a single transfer model, will cause potential information loss. Therefore, a domain adaptive approach is proposed to apply to cross domain recommendation. The concrete includes：firstly, GFK feature mapping is used to increase the consistency of shared information and reduce the loss of potential information. In order to improve the accuracy of predictions, joint user focus and item focus are used to predict missing rating. Experimental results on open source dataset demonstrate that the proposed model can improve the accuracy of recommendation.

Key words: transfer learning, recommendation technology, domain adaptation, data sparsity, feature mapping