  Online news platforms often use personalized news recommendation methods to
help users discover articles that align with their interests. These methods
typically predict a matching score between a user and a candidate article to
reflect the user's preference for the article. Some previous works have used
language model techniques, such as the attention mechanism, to capture users'
interests based on their past behaviors, and to understand the content of
articles. However, these existing model architectures require adjustments if
additional information is taken into account. Pre-trained large language
models, which can better capture word relationships and comprehend contexts,
have seen a significant development in recent years, and these pre-trained
models have the advantages of transfer learning and reducing the training time
for downstream tasks. Meanwhile, prompt learning is a newly developed technique
that leverages pre-trained language models by building task-specific guidance
for output generations. To leverage textual information in news articles, this
paper introduces the pre-trained large language model and prompt-learning to
the community of news recommendation. The proposed model "prompt-based news
recommendation" (PBNR) treats the personalized news recommendation as a
text-to-text language task and designs personalized prompts to adapt to the
pre-trained language model -- text-to-text transfer transformer (T5).
Experimental studies using the Microsoft News dataset show that PBNR is capable
of making accurate recommendations by taking into account various lengths of
past behaviors of different users. PBNR can also easily adapt to new
information without changing the model architecture and the training objective.
Additionally, PBNR can make recommendations based on users' specific
requirements, allowing human-computer interaction in the news recommendation
field.
