Fuzzy Personalized Scoring Model for Recommendation System
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng
Abstract
In this research, we aim to propose a data preprocessing framework particularly for financial sector to generate the rating data as input to the collaborative system. First, clustering technique is applied to cluster all users based on their demographic information which might be able to differentiate the customers' background. Then, for each customer group, the importance of demographic characteristics which are highly associated with financial products purchasing are analyzed by the proposed fuzzy integral technique. The importance scores across items and customers are generated either on customer groups and individuals. The analysis shows the proposed method is able to differentiate customers based on their demographic and purchasing behaviors. Also, the generated rating matrix can be directly used for collaborative filtering model.
BibTeX
@inproceedings{icassp2019_fuzzypersonalize,
title = {Fuzzy Personalized Scoring Model for Recommendation System},
author = {Chao-Lung Yang and Shang-Che Hsu and Kai-Lung Hua and Wen-Huang Cheng},
booktitle = {ICASSP 2019},
year = {2019}
}