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Collaborative Filtering Recommendation Algorithm Based on Matrix Factorization and User Nearest Neighbors

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Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2016, SCS AutumnSim 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 643))

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Abstract

The disadvantage of the traditional CFAbMD algorithm is no consideration of impact of local users’ neighbor on item rating. Aiming at this problem, a new CFAbMD algorithm is proposed considering both ALS matrix factorization and user nearest neighbor (CFAbMD-UNN), which integrates the similarity information among users into the matrix factorization of model. Furthermore, the CFAbMD-UNN algorithm was implemented in parallel on Spark. Experiments on Movielens shows that the propsosed CFAbMD-UNN algorithm outperforms the traditional CFAbMD algorithm.

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References

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Author information

Authors and Affiliations

  1. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Zhongjie Wang, Nana Yu & Jiaxian Wang

Authors
  1. Zhongjie Wang
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  2. Nana Yu
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  3. Jiaxian Wang
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Corresponding author

Correspondence to Zhongjie Wang .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Lin Zhang

  2. Beihang University, Beijing, China

    Xiao Song

  3. Beihang University, Beijing, China

    Yunjie Wu

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© 2016 Springer Science+Business Media Singapore

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Wang, Z., Yu, N., Wang, J. (2016). Collaborative Filtering Recommendation Algorithm Based on Matrix Factorization and User Nearest Neighbors. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_21

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  • DOI: https://doi.org/10.1007/978-981-10-2663-8_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2662-1

  • Online ISBN: 978-981-10-2663-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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