## 4 Results  
### Results of Store-Based Collaborative Filtering  
This paper utilized store-based collaborative filtering, with store similarity calculated based on the similarity of category-based sales. Initially, we compared the recommended products for the store with the highest similarity and the store with the lowest similarity in order to assess the precision of store similarity measurement. In the case of Store A, we selected 15 recommended products from the top 30 stores with the highest category-based similarity. These 15 recommended products were filtered based on the exclusion criteria mentioned earlier. Table 1 displays the top 15 recommended products for Store A. Table 2 presents the top 15 recommended items for Store B, which is the most similar store to Store A. Upon reviewing the items listed in Tables 1 and 2, it is evident that 10 items are being recommended identically. The 66.7% item duplication rate between the most similar stores is a result of the application of exclusion criteria such as inventory considerations. Table 3 presents 15 recommended items for Store C, which is the least similar store to Store A. Upon reviewing the items in Tables 1 and 3, only one item, \"Burdock Tea 500 mL\", is recommended identically, resulting in a very low duplication rate. In conclusion, the analysis of the recommended items in Tables 1-3 indicates that the similarity measurement based on category-wise sales, as employed in this study, is appropriate. Table 4 presents a list of the top 10 recommended items for Store A. In addition to the exclusion criteria mentioned earlier, this list has been reorganized based on business conditions (such as uniqueness, trendiness, profitability, sales growth rate, PL status, etc. ). \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Rank**} & **Product Code** & **Product Name** & **Scores** \\ \hline
1 & 123734 & Chicken breast ricotta salad & 43.5 \\
2 & 016104 & Thick Buckwheat Tea 340 mL & 37.0 \\
3 & 840169 & Aloe pet 340 mL & 30.4 \\
4 & 000022 & Corn Silk Tea 340 mL & 29.6 \\
5 & 123697 & Tandanij hot chicken tender salad & 19.9 \\
6 & 840190 & Tandanij grilled chicken breast salad & 19.6 \\
7 & 012176 & Blue zero soda 250 mL & 16.5 \\
8 & 016098 & Milk chocolate biscuit 102 g & 12.7 \\
9 & 112783 & Kim Rabbit Fresh Strawberry Sandwich & 11.0 \\
10 & 005314 & Burdock tea 500 mL & 10.2 \\
11 & 008765 & Orange mango 200 mL & 8.8 \\
12 & 008758 & Sour love plum 42 g & 7.4 \\
13 & 016135 & Choco-chip donut 38.3 g & 7.4 \\
14 & 006830 & Coconut milk plus 290 mL & 5.8 \\
15 & 040350 & Noodle Fit Spicy Udon Flavor Cup & 5.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recommended products for Store A (top 15). Furthermore, Table 4 has been further fine-tuned based on the expiration of promotions at the time of the recommendation algorithm execution. As a result, 'Thick Buckwheat Tea 340 mL' and 'Corn Silk Tea 340 mL' were excluded from the list. Additionally, 'Choco-chip' donut 38.3 g' ranked higher than 'Sour love plum 42 g' due to a higher sales growth rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Product Code** & **Product Name** & **Score** & **Sales Growth** & **PL** & **New** & **Days after** \\  & & & & **Rate** & **/PB** & **Prod.** & **Order** \\ \hline
1 & 123734 & Chicken breast ricotta salad & 43.5 & NaN & 0 & 0 & NaN \\
2 & 840169 & Aloe pet 40 mL & 30.3 & 17.6 & 0 & 0 & 45 \\
3 & 123697 & Tandanji hot chicken tender salad & 19.9 & 92.3 & 0 & 0 & NaN \\
4 & 840190 & Tandanji grilled chicken breast salad & 19.6 & 13.2 & 0 & 1 & NaN \\
5 & 012176 & Blue zero soda 250 mL & 16.5 & 22.5 & 0 & 1 & NaN \\
6 & 016098 & Milk chocolate biscuit 102 g & 12.7 & \(-\)33.2 & 0 & 0 & NaN \\
7 & 005314 & Burdock tea 500 mL & 10.2 & 10.8 & 0 & 1 & NaN \\
8 & 008765 & Orange mango 200 mL & 8.8 & NaN & 0 & 0 & NaN \\
9 & 016135 & Choco-chip donut 38.3 g & 7.4 & 35.6 & 0 & 0 & NaN \\
10 & 008758 & Sour love plum 42 g & 7.4 & 7.83 & 0 & 0 & NaN \\ \hline \hline \end{tabular}
\end{table}