Aug 23, 2019 · The existing works mainly aim at leveraging neural network to model the nonlinear representations of users and items. However, they only use ...
Currently, neutral networks attract much attention and show great potential in recommendation systems. The existing works mainly aim at leveraging neural ...
Jointing Knowledge Graph and Neural Network for Top-N ...
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The key point of JKN is to learn accurate latent representations of item attributes through knowledge graph, then to integrate them into a feedforward neural ...
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In this paper, we have proposed a new graph deep learning model associated with knowledge graph with the aim of modeling the latent feature of user and item.
Dec 15, 2023 · Knowledge graph makes informed and explainable recommendations. Our framework represents relationship between users, items and item attributes.
Missing: Jointing | Show results with:Jointing
In this paper, a graph neural network recommendation model is proposed, which introduces influencing factors in the process of learning knowledge map features.
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Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation.
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Aug 28, 2024 · • In most cases, FEGNN behaves the best both in the Top-K recommendation and CTR prediction. To validate the significance of the ...
In recent years, incorporating knowledge graphs as side information to recommender systems by knowledge graph embedding techniques has attracted considerable ...
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Ferguson et al. [47] proposed gated knowledge graph neural networks for top-n recommender systems, which takes into account the fact that the propagation of ...
Missing: Jointing | Show results with:Jointing