Abstract: Probabilistic matrix factorization is a type of collaborative filtering algorithm which is widely used in recent years.Based on the problem of how to use matrix factorization technology to improve the recommendation quality and how to breakthrough the limitation of calculation time and resource in big data environment,we introduced an improved probabilistic matrix factorization algorithm which integrates neighbor information and introduced parallel-IPMF,overcoming the problem of high calculation complex and the problem of parallelization.We used the real dataset to implement our algorithm on the MapReduce parallel computation framework.The experiment results show that our algorithm can improve the recommendation quality and reduce the computation time.

Key words: Recommendation algorithm,Probabilistic matrix factorization,Big data,MapReduce