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.
翻译:一些最近的新闻推荐(NR)方法引入了预训练语言模型(PLM)来通过遵循精心设计的推荐特定的神经网络和目标函数的vanilla预训练和微调范式来编码新闻表示。由于任务目标与PLM的不一致,我们认为他们的建模范式还没有充分利用预训练过程中嵌入的丰富语义信息和语言知识。最近,预训练、提示和预测范式,称为Prompt Learning,在自然语言处理领域取得了很多成功。在本文中,我们第一次尝试了这种新范式,开发了一种Prompt4NR框架,它将预测用户是否点击候选新闻的任务转化为填空式预测任务。具体而言,我们设计了一系列提示模板,包括离散、连续和混合模板,并构建相应的答案空间来检查所提出的Prompt4NR框架。此外,我们使用提示合成来集成来自多个提示模板的预测。对MIND数据集的大量实验验证了我们的Prompt4NR的有效性,并提供了一组新的基准结果。