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：本文研究了多视图学习中的数据宽度问题。 为了解决多视图评级中的数据宽度问题, 我们提出一个深度传输强度因子化(DTTF)的通用架构, 整合深层学习和跨部数度因子化, 将侧面信息嵌入其中, 以有效补偿高度因子化。 然后我们展示了我们架构的即时化, 将堆叠的拆卸自动编码器( SDAE) 和 CANDECOMP/ PARAFAC (CP) 和源域和目标域的拉力化结合起来, 用户和项目的侧面信息与稀薄的多视图评级紧密结合, 以及基于联合优化学习的潜在因素。 我们紧密结合了多视图评级和侧面信息, 以改善跨部数度因子化的建议。 真实世界数据集的实验结果显示, 我们的DTF计划在多视图评级预测中都超越了最先进的方法 。