基于注意力机制的深度学习推荐研究进展

近年来,注意力机制AM被广泛应用到基于深度学习的自然语言处理任务中,基于注意力机制的深度学习推荐也成为推荐系统研究的一个新方向.探讨了注意力机制的结构和分类标准,从基于注意力机制的DNN推荐、CNN推荐、RNN推荐、GNN推荐4个方面分析了现有融合注意力机制的深度学习推荐研究的主要进展和不足,阐明了其中的主要难点,最后指出了多特征交互的注意力机制推荐、多模态注意力机制深度学习推荐、融入注意力机制的多种深度神经网络混合推荐和注意力机制的群组推荐等基于注意力机制的深度学习推荐未来的主要研究方向.

Research advances on deep learning recommendation based on attention mechanism

In recent years, Attention Mechanism (AM) has been widely used in natural language processing tasks based on deep learning. Deep learning recommendation based on attention mechanism has become a new direction in the research of recommendation system. This paper discusses the structure and classification standard of attention mechanism, and analyzes the main progress and shortcomings of the existing deep learning recommendation researches based on attention mechanism from four aspects: DNN recommendation, CNN recommendation, RNN recommendation and GNN recommendation. The main difficulties in the research are illustrated. Finally, the paper points out the future direction of deep learning recommendation including multi-feature interaction attention mechanism recommendation, multi-modal attention mechanism recommendation, hybrid recommendation for multiple deep neural networks based on attention mechanism, and group recommendation based on attention mechanism.