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

Rui Li, Jianyang Li, Benkun Zhu
2015 Proceedings of the 2015 International Symposium on Computers and Informatics   unpublished
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
more » ... uch 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.
doi:10.2991/isci-15.2015.4 fatcat:mz5qq6kqpzeald74sx2ooayv7m