  Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained
Language Model (PLM) to encode news representation by following the vanilla
pre-train and fine-tune paradigm with carefully-designed
recommendation-specific neural networks and objective functions. Due to the
inconsistent task objective with that of PLM, we argue that their modeling
paradigm has not well exploited the abundant semantic information and
linguistic knowledge embedded in the pre-training process. Recently, the
pre-train, prompt, and predict paradigm, called \textit{prompt learning}, has
achieved many successes in natural language processing domain. In this paper,
we make the first trial of this new paradigm to develop a \textit{Prompt
Learning for News Recommendation} (Prompt4NR) framework, which transforms the
task of predicting whether a user would click a candidate news as a cloze-style
mask-prediction task. Specifically, we design a series of prompt templates,
including discrete, continuous, and hybrid templates, and construct their
corresponding answer spaces to examine the proposed Prompt4NR framework.
Furthermore, we use the prompt ensembling to integrate predictions from
multiple prompt templates. Extensive experiments on the MIND dataset validate
the effectiveness of our Prompt4NR with a set of new benchmark results.
