基于云‒端融合的个性化推荐服务系统  

Personalized recommendation service system based on cloud-client-convergence

作　　者：韩佳良 韩宇栋 刘譞哲[1] 赵耀帅 冯迪[2,3] HAN Jialiang;HAN Yudong;LIU Xuanzhe;ZHAO Yaoshuai;FENG Di(Key Laboratory of High Confidence Software Technologies of Ministry of Education(Peking University),Beijing 100871,China;TravelSky Technology Limited,Beijing 101318,China;Key Laboratory of Intelligent Application Technology for Civil Aviation Passenger Services,Civil Aviation Administration of China,Beijing 101318,China)

机构地区：[1]高可信软件技术教育部重点实验室(北京大学),北京100871 [2]中国民航信息网络股份有限公司,北京101318 [3]中国民用航空局民航旅客服务智能化应用技术重点实验室,北京101318

出　　处：《计算机应用》2022年第11期3506-3512,共7页journal of Computer Applications

基　　金：北大百度基金资助项目(2020BD007)。

摘　　要：主流个性化推荐服务系统通常利用部署在云端的模型进行推荐,因此需要将用户交互行为等隐私数据上传到云端,这会造成隐私泄露的隐患。为了保护用户隐私,可以在客户端处理用户敏感数据,然而,客户端存在通信瓶颈和计算资源瓶颈。针对上述挑战,设计了一个基于云‒端融合的个性化推荐服务系统。该系统将传统的云端推荐模型拆分成用户表征模型和排序模型,在云端预训练用户表征模型后,将其部署到客户端,排序模型则部署到云端;同时,采用小规模的循环神经网络(RNN)抽取用户交互日志中的时序信息来训练用户表征,并通过Lasso算法对用户表征进行压缩,从而在降低云端和客户端之间的通信量以及客户端的计算开销的同时防止推荐准确率的下跌。基于RecSys Challenge 2015数据集进行了实验,结果表明,所设计系统的推荐准确率和GRU4REC模型相当,而压缩后的用户表征体积仅为压缩前的34.8%,计算开销较低。Mainstream personalized recommendation systems usually use models deployed in the cloud to perform recommendation,so the private data such as user interaction behaviors need to be uploaded to the cloud,which may cause potential risks of user privacy leakage.In order to protect user privacy,user-sensitive data can be processed on the client,however,there are communication bottleneck and computation resource bottleneck in clients.Aiming at the above challenges,a personalized recommendation service system based on cloud-client-convergence was proposed.In this system,the cloud-based recommendation model was divided into a user representation model and a sorting model.After being pretrained on the cloud,the user representation model was deployed to the client,while the sorting model was deployed to the cloud.A small-scale Recurrent Neural Network(RNN)was used to model the user behavior characteristics by extracting temporal information from user interaction logs,and the Lasso(Least absolute shrinkage and selection operator)algorithm was used to compress user representations,thereby preventing a drop in recommendation accuracy while reducing the communication overhead between the cloud and the client as well as the computation overhead of the client.Experiments were conducted on RecSys Challenge 2015 dataset,and the results show that the recommendation accuracy of the proposed system is comparable to that of the GRU4REC model,while the volume of the compressed user representations is only 34.8%of that before compression,with a lower computational overhead.

关 键 词：个性化推荐服务系统 云‒端融合 用户表征模型 隐私保护 循环神经网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]