D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

• URL: http://arxiv.org/abs/2112.10085v2
• Date: Tue, 19 Sep 2023 09:29:28 GMT
• Title: D-HAN: Dynamic News Recommendation with Hierarchical Attention Network
• Authors: Qinghua Zhao
• Abstract summary: News recommendation models often fall short in capturing users' preferences due to their static approach to user-news interactions. 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.
• Score: 0.8702432681310401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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