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프로필 구분선 R Shiny Demo 구분선 연구실적 구분선 컨설팅구분선 갤러리
02
RESEARCH
연구분야
 

Dong-A University

Department of Global Business

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R Shiny Demo Home > R Shiny Demo  

1. The collaborative filtering approaches for the binary market basket data with the high dimensional cold-start problems

1.1. Mild, A. and Reutterer, T. (2003), An improved collaborative filtering approach for predicting cross-category purchase based on binary market basket data, Journal of Retailing and Consumer Services, 10(3), 123–133.

Source: https://www.researchgate.net/publication/221151296_Collaborative_Filtering_Methods_for_Binary_Market_Basket_Data_Analysis

1.2. Wook-Yeon Hwang (2023), New Conditional Probability-based Collaborative Filtering for the Binary Market Basket Data with the High Dimensional Cold-Start Problem, under review.

Source: Coming soon

 

2. The experimental design


Division of the experimental data set for the improved CF approach

          A : Training users
          B : Active users
          C : Training items
          D : Active items
          E : Data for calculating the similarities between A and B in the improved CF approach
          F : Test data set

Figure. Division of the experimental data set for the CF approach.

 

3. R Shiny GUI instruction

- Upload a csv file comprising zeros and ones

- Only the first 20 users are selected as training users(A), while the first 80% of the items is selected as training items(C).

- The performance measure is based on the precision, which is generally used in information retrieval research and defined by


                                Precision =    Hitting number/Top-N’                    

where ‘Top-N’ is the number of first N items that are recommended by a CF scheme and ‘Hitting number’ is the actual Top-N obtained from the section F.

- The precision for Top-N (N=1,...,10) is calculated.

 

4. R Shiny GUI link: Click here

 

 

 

 

 

 

 

 

 

 

 

 
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