Multi-criteria recommender system with Hybrid Deep Tensor Decomposition
Junhao Wang · 2021
Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings. To alleviate this problem, we propose a generic architecture of hybrid deep tensor decomposition (HDTD) by integrating deep representation learning and tensor decomposition, where the side information is incorporated as a compensation for tensor sparsity. Experimental results on three real-world datasets demonstrate that our HDTD schemes outperform state-of-the-art methods on multi-criteria rating predictions.