Abstract
In order to address data sparsity, missing value and over-fitting problems in social tagging system, a Coupled Matrix and Tensor Factorization (CMTF) method named Tagrec-CMTF for tag recommendation is proposed in this paper. In the CMTF method, we decompose the tag-item-user tensor joint with tag graph and two auxiliary matrices by using the coupled matrix and tensor factorization, optimize the learning parameters with ADMM algorithm, and recommend the tag according to the predicted tensor. Our algorithm infuses the homogeneous and heterogeneous information of the tag and provides good prediction performance. Experiment results show that Tagrec-CMTF outperforms existing methods that do not utilize the homogeneous and heterogeneous information of the tag simultaneously.
| Original language | English |
|---|---|
| Pages (from-to) | 64142-64152 |
| Journal | IEEE Access |
| Volume | 6 |
| Online published | 24 Oct 2018 |
| DOIs | |
| Publication status | Published - 2018 |
Research Keywords
- ADMM
- Coupled matrix and tensor factorization
- Matrix decomposition
- Optimization
- Prediction algorithms
- Predictive models
- Semantics
- Tag graph regularization
- Tag recommendation
- Tagging
- Tensile stress
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.