Abstract: Abstract： Personalized news recommendation technology is important to alleviate information overload and improve user experience. News recommendation models are usually iteratively trained based on fixed data sets. However， in real scenarios， news recommendation models need to constantly learn to adapt to new users and news. Therefore， incremental learning has been proposed to help models perform incremental updates. The main challenge of the incremental learning of news recommendation models is the catastrophic forgetting problem， where a trained model forgets the user preferences it has previously learned. In view of this， this paper proposes SSL-DR， an incremental learning method of news recommendation models based on self-supervised learning and data replay. SSL-DR firstly adds the self-supervised learning task to the news recommendation task to obtain the user's stable preference， which effectively reduces the problem of catastrophic forgetting. To consolidate the learned knowledge， SSL-DR further implements a sampling strategy based on the user's click probability scores for candidate news to achieve data replay and transfer the learned knowledge through a knowledge distillation strategy. The experimental results show that， our method can effectively improve the overall recommendation performance of the news recommendation model in the process of incremental training， and significantly alleviate the problem of catastrophic forgetting.

Key words: Key words： news recommendation, incremental learning, catastrophic forgetting, self-supervised learning, deep learning