[1]王健宗,肖京,朱星华,等.联邦推荐系统的协同过滤冷启动解决方法[J].智能系统学报,2021,16(1):178-185.[doi:10.11992/tis.202009032]
WANG Jianzong,XIAO Jing,ZHU Xinghua,et al.Cold starts in collaborative filtering for federated recommender systems[J].CAAI Transactions on Intelligent Systems,2021,16(1):178-185.[doi:10.11992/tis.202009032]
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WANG Jianzong,XIAO Jing,ZHU Xinghua,et al.Cold starts in collaborative filtering for federated recommender systems[J].CAAI Transactions on Intelligent Systems,2021,16(1):178-185.[doi:10.11992/tis.202009032]
联邦推荐系统的协同过滤冷启动解决方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第1期
页码:
178-185
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2021-01-05
- Title:
- Cold starts in collaborative filtering for federated recommender systems
- Keywords:
- federated learning; privacy protection; data island; recommender system; collaborative filtering; cold start; machine learning; secure inner product
- 分类号:
- TP391
- 摘要:
- 基于联邦学习的推荐系统可以在保护用户隐私的情况下，联合多方数据，提升推荐系统的性能，已经成为推荐领域的研究热点之一。联邦协同过滤是联邦推荐系统中最经典及最常用的算法之一。然而，针对联邦协同过滤系统的冷启动问题的研究工作相对较少。针对这一问题，本文提出了一种基于安全内积协议的解决方案。具体地，在系统中添加新用户或新物品时，联合多方评分矩阵，利用安全内积的方法，对多方数据进行相似矩阵的求解，从而完成推荐输出。本文在MovieLens数据集上对所述方法进行了验证。结果表明：本方法能够有效解决基于相似度的协同过滤中的冷启动问题，并且推荐效果也会依据多方数据分布的比例变化。
- Abstract:
- Recommender systems based on federated learning has become one of the research hotspots in the recommender field. However, few studies focused on the cold start problem of federated recommender systems. Under the framework of federated learning, we propose a novel collaborative filtering algorithm for its solution: through involving more rating matrices, we can get a similarity matrix with secure inner product method, and implement the recommendation for new users to the system. In this work, we verify the performance of our method on MovieLens. The results show that our proposal is effective in solving the cold start problem in similarity-based collaborative filtering, and the recommendation effects vary according to the data distribution among different parties.
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备注/Memo
收稿日期:2020-09-23。
基金项目:国家重点研发计划“云计算和大数据”重点专项(2018YFB1003503)；国家重点研发计划“高性能计算”重点专项(2018YFB0204400)；国家重点研发计划“现代服务业共性关键技术研发及应用示范”专项(2017YFB1401202)
作者简介:王健宗，高级工程师，博士，主要研究方向为联邦学习算法、金融智能平台。主持国家重点研发计划基金项目3项、校企联合课题2项，授权发明专利100余项。发表学术论文50余篇，出版著作3部;肖京，教授级高级工程师，博士，主要研究方向为人工智能与大数据分析挖掘。国际授权专利101项，授权国内发明专利109项。2019 年吴文俊人工智能科学技术奖“杰出贡献奖”获得者，发表学术论文130余篇;朱星华，博士研究生，主要研究方向为联邦学习、机器视觉算法
通讯作者:王健宗. E-mail：jzwang@188.com
更新日期/Last Update:
2021-02-25