Towards Efficient Recommendation for Films

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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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  -