  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.
