Personalized news recommender systems help users quickly find content of their interests from the sea of information. Today, the mainstream technology for personalized news recommendation is based on deep neural networks that can accurately model the semantic match between news items and users' interests. In this paper, we present \textbf{PerCoNet}, a novel deep learning approach to personalized news recommendation which features two new findings: (i) representing users through \emph{explicit persona analysis} based on the prominent entities in their recent news reading history could be more effective than latent persona analysis employed by most existing work, with a side benefit of enhanced explainability; (ii) utilizing the title and abstract of each news item via cross-view \emph{contrastive learning} would work better than just combining them directly. Extensive experiments on two real-world news datasets clearly show the superior performance of our proposed approach in comparison with current state-of-the-art techniques.

翻译：个性化新闻推荐系统能帮助用户从海量信息中快速找到感兴趣的内容。当前，基于深度神经网络的个性化新闻推荐主流技术能够精确建模新闻内容与用户兴趣之间的语义匹配。本文提出名为\textbf{PerCoNet}的新型深度学习方法，其核心创新点在于两方面：（i）基于用户近期新闻阅读历史中的显著实体进行\textit{显式个性化分析}来表征用户，比现有工作普遍采用的隐式个性化分析方法更为有效，且兼具更强的可解释性；（ii）通过跨视图的\textit{对比学习}分别利用每条新闻的标题和摘要，比简单拼接二者效果更优。在两个真实新闻数据集上的大量实验表明，我们的方法相较于当前最先进技术具有显著优越性能。