Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)

Hybrid recommendation and parallelization of movies based on spark

Authors
Mengpu Zhou, Yu Liu
Corresponding Author
Mengpu Zhou
Available Online January 2018.
DOI
10.2991/macmc-17.2018.81How to use a DOI?
Keywords
collaborative filtering, recommendation algorithm, huge data, spark.
Abstract

With the exponential growth of Internet data,the traditional stand-alone computational model has been unable to solve the real-time precise recommendation items in a complex and huge data,and the defect of traditional recommendation algorithm has become more obvious,this paper studies the collaborative filtering algorithm and matrix decomposition method,designs a parallel computing architecture based on spark,and a movie recommendation based on hybrid recommendation algorithm [1],the experimental results show that in a certain extent improves the recommendation accuracy and scalability,and has good acceleration effect.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
Series
Advances in Engineering Research
Publication Date
January 2018
ISBN
978-94-6252-439-2
ISSN
2352-5401
DOI
10.2991/macmc-17.2018.81How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Mengpu Zhou
AU  - Yu Liu
PY  - 2018/01
DA  - 2018/01
TI  - Hybrid recommendation and parallelization of movies based on spark
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 437
EP  - 442
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.81
DO  - 10.2991/macmc-17.2018.81
ID  - Zhou2018/01
ER  -