计算机工程与应用2019，Vol.55Issue(1)：64-69,148,7.DOI:10.3778/j.issn.1002-8331.1710-0160
基于数据流和点对点网络的分布式推荐算法
Online Distributed Recommendation Algorithm Based on Data Stream and Peer-to-Peer Network
摘要
Abstract
Recommendation algorithm is one of the most widely used algorithms in data mining. However, recent studies focus on static data and lack the adaptability to dynamic data. Recommendation algorithm based on data stream is the solution to this problem. Aiming at the straggler and delayed-gradient problems in using parameter server to control model training in distributed platform, a new method of using peer-to-peer parameter exchange network is proposed, and the forgetting strategy and anomaly detection ability are introduced in the training process. Algorithm is implemented on Flink and experiments on Movielens-1m. Experimental results show that the algorithm can reduce the communication cost by half, while ensuring the accuracy of recommendation.关键词
在线矩阵分解/流计算/分布式协同过滤/点对点网络Key words
online matrix factorization/stream computing/distributed collaborative filtering/peer-to-peer network分类
信息技术与安全科学引用本文复制引用
丛义昊,于艳华..基于数据流和点对点网络的分布式推荐算法[J].计算机工程与应用,2019,55(1):64-69,148,7.基金项目
国家自然科学基金(No.61702046). （No.61702046）
