Deep Transfer Tensor Factorization for Multi-View Learning

Penghao Jiang, Ke Xin, Chunxi Li
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Abstract

This paper studies the data sparsity problem in multi-view learning. To solve data sparsity problem in multi-view 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 CANDE-COMPIPARAFAC (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.
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多视图学习的深度传递张量分解
研究了多视图学习中的数据稀疏性问题。为了解决多视图评级中的数据稀疏性问题,我们提出了一种融合深度学习和跨域张量分解的深度传递张量分解(deep transfer tensor factorization, DTTF)通用架构,其中嵌入了侧信息,为张量稀疏性提供了有效的补偿。然后,我们在源域和目标域中结合堆栈去噪自编码器(SDAE)和CANDE-COMPIPARAFAC (CP)张量分解,展示了我们的架构实例化,其中用户和项目的侧信息与稀疏多视图评级紧密耦合,并基于联合优化学习潜在因素。我们将多视图评分和侧信息紧密耦合,以改进基于跨域张量分解的推荐。在真实数据集上的实验结果表明,我们的DTTF方案在多视图评级预测方面优于最先进的方法。
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