Tensor Ring decomposition for context-aware recommendation. (1st May 2023) - Wang, Wei; Sun, Guoqiang; Zhao, Siwen; Li, Yujun; Zhao, Jianli
Highlights: We propose a Tensor Ring decomposition framework for context-aware recommendation. Our framework can achieve a better balance between performance and computational. We analyze the advantages of formulating recommendation as Tensor Ring. Experiments show the superiority of the proposed framework. Abstract: As a powerful tool for processing high-dimensional data, tensor decomposition has been widely used in context-aware recommendation. Most of the current tensor decomposition-based recommendation models use CP decomposition or Tucker decomposition. In practical recommendation applications, fewer parameters limit the performance of CP decomposition, and the high-order tensor kernel makes the computational complexity of Tucker decomposition exponential. In order to improve the performance of recommendation while maintaining high computational efficiency, we propose a bias Tensor Ring decomposition framework for context-aware recommendation, which employs Tensor Ring decomposition to extract user-item-context latent factors. Comparing to current tensor decomposition-based recommendation models, our framework achieves a better balance between recommendation performance and computational complexity by Tensor Ring decomposition. This is a highly scalable recommendation framework that can easily further integrate additional information such as social trust and implicit feedback, thus used for more complex recommendation tasks. To the best of our knowledge, this work is the first to formulate context-aware recommendation as Tensor Ring decomposition process. Our in-depth analyses confirm that Tensor Ring decomposition has certain advantages over CP decomposition and Tucker decomposition. Multiple experiments on three real datasets also show that the proposed framework helps to improve the recommendation performance.