基于Spark平台的ALS加速算法研究

协同过滤推荐算法在推荐系统中发挥着重要作用,但其存在执行效率与排名精度较低的问题,交替最小二乘(ALS)算法可实现并行计算,从而提高执行效率,但是该算法数据加载与迭代收敛的时间较长。为此,将非线性共轭梯度(NCG)算法与ALS算法相结合,提出一种ALS-NCG算法,以达到加速ALS算法的目的。在Spark分布式数据处理环境中对ALS-NCG算法进行性能评估,实验结果表明,相比ALS算法,ALS-NCG算法获取高精度推荐排名时需要的迭代次数与时间更少。

Research on ALS Acceleration Algorithm Based on Spark Platform

Collaborative filtering algorithm plays an important role in recommendation system,but its execution efficiency and ranking accuracy are both low.Alternating Least Squares(ALS)algorithm can implement parallel computing,thus improving the execution efficiency,but the time between data loading and iterative convergence of the algorithm is a bit long.Therefore,by combing the Nonlinear Conjugate Gradient(NCG)algorithm and the ALS algorithm,this paper proposes an ALS-NCG algorithm to accelerate the ALS algorithm.The performance of the ALS-NCG algorithm is evaluated in the Spark distributed data processing environment.Experimental results show that compared with the ALS algorithm,the ALS-NCG algorithm needs less iterations and time to obtain high-precision recommended ranking.