卷积变换器神经协同过滤

本研究引入了卷积变换器神经协同过滤（CTNCF），一种旨在通过有效捕获用户-项目交互中高阶结构信息来增强推荐系统的新方法。CTNCF 在传统神经协同过滤（NCF）模型基础上实现了重大突破，通过无缝集成卷积神经网络（CNN）和变换器层。这种精妙的集成使模型能够熟练捕获和理解推荐系统中固有的复杂交互模式。具体而言，采用 CNN 提取用户和项目嵌入的局部特征，使模型能够捕捉数据中复杂的空间依赖性。此外，利用变换器层 enables the model to capture long-range dependencies and interactions among user and item features, thereby enhancing its ability to understand the underlying relationships in the data. To validate the effectiveness of our proposed CTNCF framework, we conduct extensive experiments on two real-world datasets. The results demonstrate that CTNCF significantly outperforms state-of-the-art approaches, highlighting its efficacy in improving recommendation system performance.