Deep Transfer Tensor Factorization for Multi-View Learning
- Resource Type
- Conference
- Authors
- Jiang, Penghao; Xin, Ke; Li, Chunxi
- Source
- 2022 IEEE International Conference on Data Mining Workshops (ICDMW) ICDMW Data Mining Workshops (ICDMW), 2022 IEEE International Conference on. :459-466 Nov, 2022
- Subject
- Computing and Processing
Deep learning
Bridges
Tensors
Conferences
Noise reduction
Data mining
Optimization
multi-view learning
tensor factorization
deep learning
side information
- Language
- ISSN
- 2375-9259
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.