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Modelling Coarse- and Fine-grained User Representation for News Recommendation

Published: 16 April 2024 Publication History

Abstract

News recommendation systems help users quickly access content of interest from a vast amount of news. The key challenge lies in obtaining accurate user and news representations. Existing news recommendation methods typically model the entire sequence of user interactions with news, which makes it difficult to capture fine-grained user representation as user interests typically change over time. In this paper, we propose two different granularities of user representation: fine-grained user representation obtained from subsequences and coarse-grained user representation obtained from the entire user browsing sequence. We combine the fine-grained and coarse-grained user representation for news recommendation (CoFi-Rec). Our approach in news recommendation has been extensively validated through experiments conducted on two realistic datasets, MIND-small and MIND-large. These experiments demonstrate the effectiveness of our approach when compared to 7 state-of-the-art news recommendation methods.

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    ICMLCA '23: Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application
    October 2023
    1065 pages
    ISBN:9798400709449
    DOI:10.1145/3650215
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    Published: 16 April 2024

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