IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3510-3521. doi: 10.1109/TNNLS.2021.3053245. Epub 2022 Aug 3.
Recently, an attention mechanism has been used to help recommender systems grasp user interests more accurately. It focuses on their pivotal interests from a psychology perspective. However, most current studies based on it only focus on part of user interests; they have not mined user preferences thoroughly. To address the above problem, we propose a novel recommendation model: comparative convolutional dynamic multi-attention (CCDMA). This model provides a more accurate approach to represent user and item features and uses multi-attention-based convolutional neural networks to extract user and item latent feature vectors dynamically. The multi-attention mechanism considers both self-attention and cross-attention. Self-attention refers to the internal attention within users and items; cross-attention is the mutual attention between users and items. Moreover, we propose an optimized comparative learning framework that can mine the ternary relationships between one user and a pair of items, focusing on their relative relationship and the internal link between a pair of items. Extensive experiments on several real-world data sets show that the CCDMA model significantly outperforms state-of-the-art baselines in terms of different evaluation metrics.
最近，注意力机制被用于帮助推荐系统更准确地掌握用户兴趣。它从心理学的角度关注用户的关键兴趣。然而，目前大多数基于注意力机制的研究只关注用户兴趣的一部分，没有彻底挖掘用户偏好。针对上述问题，我们提出了一种新颖的推荐模型：对比卷积动态多注意力（CCDMA）。该模型提供了一种更准确的方法来表示用户和项目的特征，并使用基于多注意力的卷积神经网络动态提取用户和项目潜在特征向量。多注意力机制同时考虑了自注意力和交叉注意力。自注意力是指用户和项目内部的内部注意力；交叉注意力是用户和项目之间的相互注意力。此外，我们提出了一种优化的对比学习框架，可以挖掘一对用户和一对项目之间的三元关系，重点关注它们的相对关系和一对项目之间的内部联系。在几个真实数据集上的广泛实验表明，与最先进的基线相比，CCDMA 模型在不同的评估指标上都有显著的提升。
