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R Shiny Demo

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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 (2025), New Conditional Probability-based Collaborative Filtering for the Binary Market Basket Data with the High Dimensional Cold-Start Problem, Information Sciences, Volume 689, 121475.

Source: New conditional probability-based collaborative filtering for the binary market basket data with the high dimensional cold-start problem - ScienceDirect

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