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Semi-sparse algorithm based on multi-layer optimization for recommender system

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Abstract

Similarity among vectors is basic knowledge required to carry out recommendation and classification in recommender systems, which support personalized recommendation during online interactions. In this paper, we propose a Semi-sparse Algorithm based on Multi-layer Optimization to speed up the Pearson Correlation Coefficient, which is conventionally used in obtaining similarity among sparse vectors. In accelerating the batch of similarity-comparisons within one thread, the semi-sparse algorithm spares out over-reduplicated accesses and judgements on the selected sparse vector by making this vector dense locally. Moreover, a reduce-vector is proposed to restrict using locks on critical resources in the thread-pool, which is wrapped with Pthreads on a multi-core node to improve parallelism. Furthermore, among processes in our framework, a shared zip file is read to cut down messages within the Message Passing Interface package. Evaluation shows that the optimized multi-layer framework achieves a brilliant speedup on three benchmarks, Netflix, MovieLens and MovieLen1600.

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Notes

  1. http://www.mcs.anl.gov/research/projects/mpich2/.

  2. http://www.grouplens.org/node/73.

  3. http://www.netflixprize.com.

  4. http://www.cs.cmu.edu/afs/cs/project/theo-20/www/data.

  5. http://kdd.ics.uci.edu/databases/20newsgroups.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments on this paper. This work was supported in part by NSFC 61003012, 863 Program of China 2011AA01A202, and Program for Changjiang Scholars and Innovative Research Team in University (IRT1158, PCSIRT), China. The views expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Shanghai Jiao Tong University, Dongchuan 800, Shanghai, P.R. China

    Hu Guan, Huakang Li & Minyi Guo

  2. Department of Electrical and Computer Engineering, Wayne State University, 5050 Anthony Wayne Dr., Detroit, MI, USA

    Cheng-Zhong Xu

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  1. Hu Guan
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  2. Huakang Li
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  3. Cheng-Zhong Xu
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  4. Minyi Guo
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Correspondence to Minyi Guo.

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Guan, H., Li, H., Xu, CZ. et al. Semi-sparse algorithm based on multi-layer optimization for recommender system. J Supercomput 66, 1418–1437 (2013). https://doi.org/10.1007/s11227-012-0830-6

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