论文标题
DCAN：具有覆盖范围的网络的多元化新闻建议
DCAN: Diversified News Recommendation with Coverage-Attentive Networks
论文作者
论文摘要
基于自我注意力的模型被广泛用于新闻推荐任务。但是，以前的注意体系结构不会限制用户的历史行为中的重复信息，这限制了隐藏表示的力量，并导致一些问题，例如信息冗余和过滤器气泡。为了解决此问题，我们提出了一个个性化的新闻推荐模型，称为DCAN。它通过新闻编码器和用户编码器捕获了多元融合的用户新匹配信号。我们不断更新覆盖矢量，以跟踪新闻关注的历史并以4种类型的方式增强向量。然后，我们将增强的覆盖矢量喂入了多头自我注意力专注模型，以帮助调整未来的关注并将覆盖范围调节添加到损失函数（CRL）中，这使建议系统能够考虑有关差异化信息的更多信息。 Microsoft新闻推荐数据集（Mind）进行的广泛实验表明，我们的模型以最少的准确性牺牲了新闻建议的多样性。
Self-attention based models are widely used in news recommendation tasks. However, previous Attention architecture does not constrain repeated information in the user's historical behavior, which limits the power of hidden representation and leads to some problems such as information redundancy and filter bubbles. To solve this problem, we propose a personalized news recommendation model called DCAN.It captures multi-grained user-news matching signals through news encoders and user encoders. We keep updating a coverage vector to track the history of news attention and augment the vector in 4 types of ways. Then we fed the augmented Coverage vector into the Multi-headed Self-attention model to help adjust the future attention and added the Coverage regulation to the loss function(CRL), which enabled the recommendation system to consider more about differentiated information. Extensive experiments on Microsoft News Recommendation Dataset (MIND) show that our model significantly improve the diversity of news recommendations with minimal sacrifice in accuracy.
