联邦个性化学习推荐系统研究

Research on Federated Personalized Learning Recommendation System

当前,个性化学习推荐系统面临数据隐私保护、"冷启动"和法律约束等问题,而联邦学习作为近年来优秀的数据隐私保护机器学习技术解决方案,可有效解决这些问题。基于此,文章将联邦学习和个性化学习推荐相结合,设计了联邦个性化学习推荐系统。首先,文章分析了联邦个性化学习推荐系统的具体应用场景,包括横向联邦、纵向联邦、联邦强化三种。其次,文章分别针对这三种应用场景设计了相应的应用解决方案。最后,文章探讨了未来联邦个性化学习推荐系统面临的严峻挑战,以期帮助教育利益相关者在保护数据隐私的同时共享数据价值,最终实现更安全、更高质量的个性化学习推荐服务。

At present,personalized learning recommendation systems are faced with problems including data privacy protection,“cold start”,and legal constraints,and as an excellent machine learning technology solution for data privacy protection in recent years,federated learning can effectively solve these problems.Based on this,this paper combined federated personalized learning recommendation and personalized learning recommendation,and designed a federated personalized learning recommendation system.Firstly,this paper analyzed the specific application scenarios of the federated personalized learning recommendation system,which included horizontal federation,vertical federation,and federation reinforcement.Secondly,corresponding application solutions for the three application scenarios were designed.Finally,this paper discussed the severe challenges faced by the future federated personalized learning recommendation system,in order to help education stakeholders share data value while protecting data privacy,and ultimately to achieve a safer and higher quality personalized learning recommendation service.