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- W4385300868 abstract "Personalized news recommendation aims to help users find the news of their interests and alleviate information overload. Accurately modeling user interactions is the key to mining user interests. Existing methods usually learn user interactions via sequential or attention models, which ignores the complex transformational relations in user behavior sequences. Although graph neural network can solve the above problem to a certain extent by aggregating neighbor information, it only can propagate information from adjacent item, while in reality user interactions are often high-order. In this paper, we propose a novel method to extract user interests and interactions, which constructs each user behavior sequence as a hypergraph to capture high-order interactions. Specifically, we use knowledge graph as auxiliary information to enrich news semantics, thus enhancing the representation of nodes in the hypergraph. Then, we stack multiple convolutional layers to model the multi-hop high-order information propagation in the hypergraph. Furthermore, we explore time series representations in user behavior sequences to enhance user interest. Extensive experiments on two real-world datasets show the effectiveness of our method in improving news recommendation performance." @default.
- W4385300868 created "2023-07-28" @default.
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- W4385300868 date "2022-12-01" @default.
- W4385300868 modified "2023-10-16" @default.
- W4385300868 title "Capturing High-order Interactions for Interest-aware News Recommendation" @default.
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- W4385300868 doi "https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00267" @default.
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