Recommendation Algorithm Based on Restricted Boltzmann Machine and Item Type

DOI

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.

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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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  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 238
EP  - 244
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.42
DO  - 10.2991/amcce-18.2018.42
ID  - He2018/05
ER  -