This paper studies the data sparsity problem in multi-view learning. To solve data sparsity problem in multiview ratings, we propose a generic architecture of deep transfer tensor factorization (DTTF) by integrating deep learning and cross-domain tensor factorization, where the side information is embedded to provide effective compensation for the tensor sparsity. Then we exhibit instantiation of our architecture by combining stacked denoising autoencoder (SDAE) and CANDECOMP/ PARAFAC (CP) tensor factorization in both source and target domains, where the side information of both users and items is tightly coupled with the sparse multi-view ratings and the latent factors are learned based on the joint optimization. We tightly couple the multi-view ratings and the side information to improve cross-domain tensor factorization based recommendations. Experimental results on real-world datasets demonstrate that our DTTF schemes outperform state-of-the-art methods on multi-view rating predictions.

翻译：本文研究了多视角学习中的数据稀疏性问题。为了解决多视角评分中的数据稀疏问题，我们提出了一种名为深度传递张量分解（DTTF）的通用架构，该架构融合了深度学习和跨域张量分解技术，通过嵌入辅助信息有效补偿张量的稀疏性。随后，我们展示了该架构的具体实例化：在源域和目标域中，结合堆叠去噪自编码器（SDAE）与CANDECOMP/PARAFAC（CP）张量分解，其中用户和项目的辅助信息与稀疏的多视角评分紧密耦合，潜在因子基于联合优化进行学习。我们将多视角评分与辅助信息紧密关联，以改进基于跨域张量分解的推荐系统。在真实数据集上的实验结果表明，我们的DTTF方案在多视角评分预测方面优于现有最先进方法。