With the rapid development of network technology, recommendation systems attract increasing research because of its wide applications in e-commerce. Nevertheless, most existing recommendation models based on graph neu...
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ISBN:
(纸本)9789819614899;9789819614905
With the rapid development of network technology, recommendation systems attract increasing research because of its wide applications in e-commerce. Nevertheless, most existing recommendation models based on graph neural networks do not consider the transitivity of subgraph structures in interactive data. This makes the models unable to capture the complex dependencies and mutual influences between users and items, resulting in the inability to achieve high-quality personalized recommendations. To address the above challenge, we propose a novel recommendation algorithm based on knowledge graph with high-order graph convolutional network, named KG(2)CN. Firstly, we introduce the subgraph structure on the knowledge graph to capture high-order contextual information between users and items. Secondly, the mined subgraph information and graph convolutional network are combined to learn high-order features of users and items. Finally, the decoder is applied to predict the ratings of target users on the uninteracted items, thereby recommending the Top-K items. Experimental results on the Book-Crossing and *** datasets show that the proposed KG(2)CN obtains better performance in F1 score and AUC metrics.
Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representatio...
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Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human-machine system applications.
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