CBR-Recommendation System on Massive Contents Processing Using Optimized MFNN Algorithm

DOI

10.2991/isci-15.2015.4How to use a DOI?

Keywords

CBR-Recommendation System; Optimized MFNN Algorithm; Automatic Retrieval; Massive Contents

Abstract

Though recommendation systems have been widely used for websites to generate new recommendations based on like-minded users’ preferences, IEEE Internet Computing points out that current system can not meet the real large-scale e-commerce demands, and has some weakness such as low precision and slow reaction. Huge personalized data are the key to successfully give a new recommendation, but they are difficultly dealt with for they are massive with high dimensional; addressing such problems, the paper suggests to use multi-layer feed-forward neural networks (MFNN) system based on case intelligence to partition massive personalized data into the most similar groups. The subsequent experiment indicates that our system model is constructive and understandable, and our algorithm can decrease the complexity of ANN algorithm, for which the system performance can be guaranteed.

Copyright

© 2015, 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  - Rui Li
AU  - Jianyang Li
AU  - Benkun Zhu
PY  - 2015/01
DA  - 2015/01
TI  - CBR-Recommendation System on Massive Contents Processing Using Optimized MFNN Algorithm
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 22
EP  - 28
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.4
DO  - 10.2991/isci-15.2015.4
ID  - Li2015/01
ER  -