联邦推荐系统综述

朱智韬; 司世景; 王健宗; 肖京

doi:10.11959/j.issn.2096-0271.2022032

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联邦推荐系统综述

研究 | 更新时间：2024-06-03

- 联邦推荐系统综述
- Survey on federated recommendation systems
- 大数据 2022年8卷第4期页码：105-132
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518063
  2. 中国科学技术大学，安徽合肥 230026
- 作者简介：
  
  [ "朱智韬（1996- ），男，中国科学技术大学硕士生，平安科技（深圳）有限公司算法工程师，中国计算机学会（CCF）会员，主要研究方向为人工智能、联邦学习和推荐系统等" ]
  [ "司世景（1988- ），男，博士，平安科技（深圳）有限公司资深算法研究员，中国科学技术大学硕士生企业导师， CCF会员。发表机器学习、大数据和人工智能领域国际核心论文20余篇" ]
  [ "王健宗（1983- ），男，博士，平安科技（深圳）有限公司副总工程师、资深人工智能总监。CCF理事、杰出会员， CCF大数据专家委员会委员，主要研究方向为联邦学习、深度学习、云计算、物联网和元宇宙" ]
  [ "肖京（1972- ），男，博士，平安科技（深圳）有限公司首席科学家，深圳市政协委员，中国计算机学会深圳会员活动中心副主席，清华大学、上海交通大学、同济大学、香港中文大学、深圳大学、上海纽约大学客座教授，长期从事人工智能与大数据分析挖掘相关领域研究工作，发表计算机图形学、自动驾驶、3D显示、医疗诊断、联邦学习等领域国际核心论文230余篇，授权专利220余项" ]
- 基金信息：
  
  广东省重点领域研发计划“新一代人工智能”重大专项;The Key Research and Development Program of Guangdong Province(2021B0101400003)
- DOI：10.11959/j.issn.2096-0271.2022032
  中图分类号： TP391
- 网络首发：2022-07，
  
  纸质出版：2022-07-15
- 稿件说明：
移动端阅览
朱智韬, 司世景, 王健宗, 等. 联邦推荐系统综述[J]. 大数据, 2022,8(4):105-132.

Zhitao ZHU, Shijing SI, Jianzong WANG, et al. Survey on federated recommendation systems[J]. Big data research, 2022, 8(4): 105-132.
朱智韬, 司世景, 王健宗, 等. 联邦推荐系统综述[J]. 大数据, 2022,8(4):105-132. DOI： 10.11959/j.issn.2096-0271.2022032.

Zhitao ZHU, Shijing SI, Jianzong WANG, et al. Survey on federated recommendation systems[J]. Big data research, 2022, 8(4): 105-132. DOI： 10.11959/j.issn.2096-0271.2022032.

摘要

在联邦学习范式中，原始数据被本地存储在独立的用户客户端中，而脱敏数据被发送到中心服务器中加以聚合，这给众多领域提供了一种新颖的设计思路。考虑到传统推荐系统的研究方向集中于提高推荐效果，在资源节约、跨领域推荐、隐私保护等方面还具有很大改进空间，如何将联邦学习与推荐系统结合以解决这些问题成为当前的一个研究热点。对近年来基于联邦学习的推荐系统进行了全面的总结、比较与分析，首先介绍了推荐系统的传统实现方式及面临的瓶颈；然后引入了联邦学习范式，描述了联邦学习在隐私保护、利用多领域用户数据两方面给推荐系统带来的增益，以及二者结合的技术挑战，进而详细说明了现有的联邦推荐系统部署方式；最后，对联邦推荐系统未来的研究进行了展望与总结。

Abstract

In the federated learning (FL) paradigm

the original data are stored in independent clients while masked data are sent to a central server to be aggregated

which proposes a novel design approach to numerous domains.Given the wide application of recommendation systems (RS) in diverse domains

combining RS with FL techniques has been gaining momentum to reduce the computational cost

do cross-domain recommendation and protect users’ privacy while maintaining recommendations performance as traditional RS.The federated learning-based recommendation systems in recent years were comprehensively summarized.The difference between traditional and federated recommendation systems was analyzed

and the main research direction and progress of federated recommendation systems were demonstrated with comparison and analysis.Firstly

the traditional recommendation systems and their bottleneck were summarized.Then the federated learning paradigm was introduced.Furthermore

the advantages of combining federated learning with recommendation systems were depicted in two aspects: privacy protection and usage of multi-domain user information

along with the technical challenges during the combination.At the same time

the existing deployment of federated recommendation systems was illustrated in detail.Finally

future research on federated recommendation systems was prospected and summarized.

关键词

Keywords

references

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