【摘要】 概率矩阵分解是近几年广泛应用的协同过滤推荐方法。针对如何利用矩阵分解技术提高推荐质量以及在大数据环境下如何突破计算时间、计算资源瓶颈等问题进行研究,提出了Improved Probabilistic Matrix Factorization(IPMF)融入邻居信息的概率矩阵分解算法,并且提出了parallel-IPMF(p-IPMF)算法来解决融入邻居信息后计算复杂度高和难以并行化等问题。在MapReduce并行计算框架下将p-IPMF算法加以实现,并在真实数据集上进行验证。实验结果表明,所提算法能有效提高推荐质量并缩短计算时间。更多还原

【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.更多还原