计算机与现代化Issue(12)：1-6,6.DOI:10.3969/j.issn.1006-2475.2023.12.001
基于自监督学习和数据回放的新闻推荐模型增量学习方法
Incremental News Recommendation Method Based on Self-supervised Learning and Data Replay
摘要
Abstract
Personalized news recommendation technology is important to alleviate information overload and improve user experi-ence.News recommendation models are usually iteratively trained based on fixed data sets.However,in real scenarios,news rec-ommendation 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.关键词
新闻推荐/增量学习/灾难性遗忘/自监督学习/深度学习Key words
news recommendation/incremental learning/catastrophic forgetting/self-supervised learning/deep learning引用本文复制引用
林威..基于自监督学习和数据回放的新闻推荐模型增量学习方法[J].计算机与现代化,2023,(12):1-6,6.基金项目
国家自然科学基金资助项目(62172166,61772366) （62172166,61772366）
广东省基础与应用基础研究基金资助项目(2022A1515011380) （2022A1515011380）
上海市自然科学基金资助项目(17ZR1445900) （17ZR1445900）
