CoATF: Convolution and Attention Based Tensor Factorization Model for Context-Aware Recommendation

Hao Li, Jianli Zhao, Qingqian Guan, Lutong Yao, Jianjian Chen, Guojun Sheng

Abstract: Tensor factorization is an effective tool that has been successfully applied in the field of context-aware recommendation. However, most existing factorization models assume a multilinear relationship between recommendation rating entries and their corresponding factors, whereas in reality, real-world tensors often contain more complex interactions. In addition, recommendation data usually exhibits sparsity, which limits the amount of information that can be learned. In order to solve the above problems, this paper proposes a new nonlinear tensor factorization model called Convolution and Attention based Tensor Factorization (CoATF). First, we introduce a more generalized implicit feedback to comprehensively represent user preference. Next, a two-layer convolutional neural network is used to model the interactions between tensor factors. Finally, the attention mechanism is utilized to weight the features and improve the robustness of the model. The results of extensive experiments on multiple context-aware recommendation tensors show that the CoATF model significantly outperforms linear and nonlinear state-of-the-art tensor decomposition correlation models with superior recommendation performance.

External IDs:dblp:journals/tnse/LiZGYCS25