Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)

Towards Efficient Recommendation for Films

Authors
Qiong Jia, Jing Zhou
Corresponding Author
Qiong Jia
Available Online September 2017.
DOI
10.2991/icmmcce-17.2017.212How to use a DOI?
Keywords
Classification Accuracy; Collaborative Filtering; Expectation Maximization Algorithm; K-nearest Neighbour; Personalized Recommendation
Abstract

We first examine the techniques, development, and application future of the current recommender systems in the film industry. Various recommendation techniques in current applications and the K-nearest neighbor (aka. KNN) algorithm, in particular, is then introduced in detail. This is followed by an introduction to the Expectation Maximization (aka. EM) algorithm based on the Bayesian classifier, which has been applied to the classification and similarity calculations of films. Finally, the movie_reviews data in the NLTK (Natural Language Toolkit) library is used to facilitate experiments. We evaluate the classification accuracy of the KNN algorithm and the EM algorithm based on the Bayesian classifier. The experimental results demonstrate that, the classification accuracy of the EM algorithm for films is higher than that of the KNN algorithm and it is feasible and useful to apply the EM algorithm to films classification.

Copyright
© 2017, 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 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
Series
Advances in Engineering Research
Publication Date
September 2017
ISBN
978-94-6252-381-4
ISSN
2352-5401
DOI
10.2991/icmmcce-17.2017.212How to use a DOI?
Copyright
© 2017, 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  - Qiong Jia
AU  - Jing Zhou
PY  - 2017/09
DA  - 2017/09
TI  - Towards Efficient Recommendation for Films
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 1198
EP  - 1204
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.212
DO  - 10.2991/icmmcce-17.2017.212
ID  - Jia2017/09
ER  -