Integrating Knowledge Graphs (KGs) into recommendation systems as supplementary information has become a prevalent strategy. By leveraging the semantic relationships between entities in KGs, recommendation systems can better comprehend user preferences. Due to the unique structure of KGs, methods based on Graph Neural Networks (GNNs) have emerged as the current technical trend. However, existing GNN-based methods struggle to (1) filter out noisy information in real-world KGs, and (2) differentiate the item representations obtained from the knowledge graph and bipartite graph. In this paper, we introduce a novel model called Attention-enhanced and Knowledge-fused Dual item representations Network for recommendation (namely AKDN) that employs attention and gated mechanisms to guide aggregation on both knowledge graphs and bipartite graphs. In particular, we firstly design an attention mechanism to determine the weight of each edge in the information aggregation on KGs, which reduces the influence of noisy information on the items and enables us to obtain more accurate and robust representations of the items. Furthermore, we exploit a gated aggregation mechanism to differentiate collaborative signals and knowledge information, and leverage dual item representations to fuse them together for better capturing user behavior patterns. We conduct extensive experiments on two public datasets which demonstrate the superior performance of our AKDN over state-of-the-art methods, like Knowledge Graph Attention Network (KGAT) and Knowledge Graph-based Intent Network (KGIN).

In the field of image processing, better results can often be achieved through the deepening of neural network layers involving considerably more parameters. In image classification, improving classification accuracy without introducing too many parameters remains a challenge. As for image conversion, the use of the conversion model of the generative adversarial network often produces semantic artifacts, resulting in images with lower quality. Thus, to address the above problems, a new type of attention module is proposed in this paper for the first time. This proposed approach uses the pixel–channel hybrid attention (PCHA) mechanism, which combines the attention information of the pixel and channel domains. The comparative results of using different attention modules on multiple-image data verify the superiority of the PCHA module in performing classification tasks. For image conversion, we propose a skip structure (S-PCHA model) in the up- and down-sampling processes based on the PCHA model. The proposed model can help the algorithm identify the most distinctive semantic object in a given image, as this structure effectively realizes the intercommunication of encoder and decoder information. Furthermore, the results showed that the attention model could establish a more realistic mapping from the source domain to the target domain in the image conversion algorithm, thus improving the quality of the image generated by the conversion model.