SI-News: Integrating social information for news recommendation with attention-based graph convolutional network
SI-News: Integrating social information for news recommendation with attention-based graph convolutional network
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DOI：
10.1016/j.neucom.2022.04.073
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发表时间：
2022-04
期刊：
影响因子：
6
通讯作者：
Peng Zhu;Dawei Cheng;Siqiang Luo;Fan Yang;Yifeng Luo;Weining Qian;Aoying Zhou
中科院分区：
文献类型：
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作者：
Peng Zhu;Dawei Cheng;Siqiang Luo;Fan Yang;Yifeng Luo;Weining Qian;Aoying Zhou
高质量的新闻推荐在很大程度上依赖于新闻文档和用户兴趣的准确和及时的表示。社交信息通常包含用户及其好友的最新活动信息，自然反映了用户兴趣的动态和多样性。然而，现有的新闻推荐方法往往忽略了这些动态项，从而导致性能不佳。在本文中，我们提出了一种新颖的方法，即通过注意图卷积网络（GCN）从用户的社交信息中嵌入用户的兴趣。我们还通过关注机制共同优化新闻的标题和内容，从而提高新闻的表现形式。在三个基准数据集上的大量实验表明，与最先进的基线相比，我们的方法有效地提高了新闻推荐性能。我们还在真实数据集上评估了我们的模型，结果证明了所提出的技术在工业级应用中的卓越性能。
High-quality news recommendation heavily relies on accurate and timely representations of news documents and user interests. Social information, which usually contains the most recent information about the activities of users and their friends, naturally reflects the dynamics and diversities of user interests. However, existing news recommendation approaches often overlook these dynamic items, and thus lead to suboptimal performance. In this paper, we propose a novel approach by embedding users’ interests from their social information by attentional graph convolutional network (GCN). We also improve news representations by jointly optimizing the titles and contents of news via attention mechanisms. Extensive experiments on three benchmark datasets show that our approach effectively improves news recommendation performance compared with state-of-the-art baselines. We also evaluate our model on a real-world dataset and the results demonstrate the superior performance of the proposed techniques in industry-level applications.