Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

Recommendation Algorithm Based on Restricted Boltzmann Machine and Item Type

Authors
Fan He, Na Li, Zhi-gang Zhang
Corresponding Author
Fan He
Available Online May 2018.
DOI
https://doi.org/10.2991/amcce-18.2018.42How to use a DOI?
Keywords
Restricted Boltzmann Machine, collaborative filtering, recommendation system, item type.
Abstract
Because of the sparsity of the ratings in the recommendation system, the calculation of the neighbors will be affected. The common method is to predict the missing ratings and calculate the neighbors with the prediction ratings. However, due to the deviation between prediction ratings and true ratings, it will also lead to the inaccuracy of nearest neighbors. In order to solve this problem, we use RBM to predict the missing ratings. Considering that the type or label of the item has certain influence on the rating, we introduce the type similarity of the item to modify the original neighbors. So that we get the neighbors which is closer to the target user. In this paper, the new model is applied to the MovieLens data set. The result shows that the results of the new model are better than collaborative filtering based on RBM and collaborative filtering based on SVD.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.42How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Fan He
AU  - Na Li
AU  - Zhi-gang Zhang
PY  - 2018/05
DA  - 2018/05
TI  - Recommendation Algorithm Based on Restricted Boltzmann Machine and Item Type
BT  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.42
DO  - https://doi.org/10.2991/amcce-18.2018.42
ID  - He2018/05
ER  -