Soft Prompt-Tuning for Personalized News Recommendation

Personalized news recommendation aims to address information overload problems and find interesting news for users, which is essential for online news portals and platforms nowadays. Existing deep neural networks and pretrained language models require some labeled data to train end-to-end models or fine-tune object functions. More recently, prompt-tuning methods have been proposed to minimize the gap between the knowledge acquired during pretraining and the fine-tuned model. Although these methods have achieved fairly good results in news recommendation, how to take the personalized characteristics of items into template construction and achieve competitive performance compared to hand-crafted prompts through soft prompt-tuning is still an important academic and practical challenge. In this article, we propose a soft prompt-tuning method for personalized news recommendation. The side information of news is introduced to learn the characteristics of items in the recommendation, and different strategies of verbalizer optimization are designed for performance improvement. Specifically, the summaries and subcategories of news are first introduced for template construction to consider the characteristics of news, which can provide a more comprehensive and accurate description of news. Secondly, three different strategies are designed to expand the label word space for modifying soft prompts, and the integration of these strategies is used for final verbalizer optimization, which can significantly reduce additional noise and improve recommendation accuracy. Extensive experiments conducted on the MINDsmall and MINDlarge datasets validated the effectiveness of the proposed method compared to other state-of-the-art prompt methods.