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D-HAN: Dynamic News Recommendation with Hierarchical Attention Network (2112.10085v2)

Published 19 Dec 2021 in cs.IR and cs.AI

Abstract: News recommendation models often fall short in capturing users' preferences due to their static approach to user-news interactions. To address this limitation, we present a novel dynamic news recommender model that seamlessly integrates continuous time information to a hierarchical attention network that effectively represents news information at the sentence, element, and sequence levels. Moreover, we introduce a dynamic negative sampling method to optimize users' implicit feedback. To validate our model's effectiveness, we conduct extensive experiments on three real-world datasets. The results demonstrate the effectiveness of our proposed approach.

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Authors (1)
  1. Qinghua Zhao (26 papers)
Citations (1)
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