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.
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References
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM, New York (1999)
Kong, S.Y., Shan Lee, L.: Improved spoken document summarization using probabilistic latent semantic analysis. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2006, vol. I, pp. 941–944 (2006)
Newman, D.J., Block, S.: Probabilistic topic decomposition of an eighteenth-century american newspaper. J. Am. Soc. Inf. Sci. Technol. 57(15), 753–767 (2006)
Wan, R., Anh, V.N., Mamitsuka, H.: Efficient probabilistic latent semantic analysis through parallelization. In: Lee, G.G., Song, D., Lin, C.-Y., Aizawa, A., Kuriyama, K., Yoshioka, M., Sakai, T. (eds.) AIRS 2009. LNCS, vol. 5839, pp. 432–443. Springer, Heidelberg (2009)
Hong, C., Chen, W., Zheng, W., Shan, J., Chen, Y., Zhang, Y.: Parallelization and characterization of probabilistic latent semantic analysis. In: Proc. 37th International Conference on Parallel Processing, pp. 628–635 (2008)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters, pp. 10–10. USENIX Association (2004)
Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 197–205. ACM Press, New York (2004)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(27), 89–115 (2007)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B 39(1), 1–38 (1977)
White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Inc., Sebastopol (2009)
MovieLens: Movielens datasets of the university of minnesota, http://www.movielen.org
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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. https://doi.org/10.1007/978-3-642-23878-9_46
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DOI: https://doi.org/10.1007/978-3-642-23878-9_46
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