A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. %Modeling such multiple interests is critical for precise news recommendation. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models.
翻译：以会话为基础的新闻建议系统向用户推荐下一则新闻,办法是以她/他在会话中阅读/点击的新闻序列中所包含的潜在利益为模型,向用户提供下一个新闻。一般而言,用户的利益是多种多样的,即对不同类型的新闻有多种利益,例如不同主题的新闻,在会话中。% 将这种多重利益进行模型化对准确的新闻建议至关重要。然而,大多数现有方法通常忽略了这种重要特点,因此没有区分和模拟用户的潜在多重利益,从而妨碍了对下一份新闻的准确建议。因此,本文为新闻建议提出了多种利益新闻序列模式。在MINS中,设计了一个基于自我注意的、以学习对每件新闻的信息嵌入为主的新闻编码器,然后设计了一个新的平行利益网络,以提取新闻序列中的潜在的多重利益,为以后的下一个新闻建议作准备。一个真实世界数据集的实验结果显示,我们的模型的性能优于最先进的比较模型。
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