关键词: 在线矩阵分解, 流计算, 分布式协同过滤, 点对点网络

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.

Key words: online matrix factorization, stream computing, distributed collaborative filtering, peer-to-peer network