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Fuzzy Personalized Scoring Model for Recommendation System

Citation Author(s):
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng
Submitted by:
Chao-Lung Yang
Last updated:
8 May 2019 - 7:40am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Chao-Lung Yang
Paper Code:
3505

Abstract 

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.

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Dataset Files

Poster_design_20190508_final.pdf

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