P2LSA and P2LSA+: Two Paralleled Probabilistic Latent Semantic Analysis Algorithms Based on the MapReduce Model

  • Yan Jin
  • Yang Gao
  • Yinghuan Shi
  • Lin Shang
  • Ruili Wang
  • Yubin Yang
Conference paper

DOI: 10.1007/978-3-642-23878-9_46

Volume 6936 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Jin Y., Gao Y., Shi Y., Shang L., Wang R., Yang Y. (2011) P2LSA and P2LSA+: Two Paralleled Probabilistic Latent Semantic Analysis Algorithms Based on the MapReduce Model. In: Yin H., Wang W., Rayward-Smith V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg

Abstract

Two novel paralleled Probabilistic Latent Semantic Analysis (PLSA) algorithms based on the MapReduce model are proposed, which are P2LSA and P2LSA+, respectively. When dealing with a large-scale data set, P2LSA and P2LSA+ 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 P2LSA, 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 P2LSA, the Map function in P2LSA+ 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 P2LSA and P2LSA+. 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 P2LSA+ is about 3 times faster than P2LSA.

Keywords

Paralleled PLSA PLSA MapReduce 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yan Jin
    • 1
  • Yang Gao
    • 1
  • Yinghuan Shi
    • 1
  • Lin Shang
    • 1
  • Ruili Wang
    • 2
  • Yubin Yang
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand