P~2LSA and P~2LSA+: Two Paralleled Probabilistic Latent Semantic Analysis Algorithms Based on the MapReduce Model

摘要

Two novel paralleled Probabilistic Latent Semantic Analysis (PLSA) algorithms based on the MapReduce model are proposed, which are P~2LSA and P~2LSA+, respectively. When dealing with a large-scale data set, P~2LSA and P~2LSA+ can improve the computing speed with the Hadoop platform. The Expectation-Maximization (EM) algorithm is often used in the traditional PLSA method to estimate two hidden parameter vectors, while the parallel PLSA is to implement the EM algorithm in parallel. The EM algorithm includes two steps: E-step and M-step. In P~2LSA, the Map function is adopted to perform the E-step and the Reduce function is adopted to perform the M-step. However, all the intermediate results computed in the E-step need to be sent to the M-step. Transferring a large amount of data between the E-step and the M-step increases the burden on the network and the overall running time. Different from P~2LSA, the Map function in P~2LSA+ performs the E-step and M-step simultaneously. Therefore, the data transferred between the E-step and M-step is reduced and the performance is improved. Experiments are conducted to evaluate the performances of P~2LSA and P~2LSA+. The data set includes 20000 users and 10927 goods. The speedup curves show that the overall running time decrease as the number of computing nodes increases. Also, the overall running time demonstrates that P~2LSA+ is about 3 times faster than P~2LSA.