## 3 Method of the Study
### Recommendation Model  
In this study, user-based collaborative filtering, a type of collaborative filtering, was chosen as the initial approach for developing the product recommendation model. The approach for establishing the product recommendation model is shown in Figure 2.  
Figure 1: Overall research process. **Select Store Characteristics for Similarity Measurement:** Defining the characteristics of similar stores is crucial for utilizing user-based collaborative filtering. Store similarity can be delineated through various methods, such as by physical attributes such as store size and location and operational characteristics such as handled products and services, as well as customer attributes such as gender and age group. In this study, the recommendation system aims to suggest products that are well sold in similar stores to a specific Store A, which currently does not offer those products. While Company E classifies stores into N commercial areas based on geographical location, there exists substantial variance among stores within each commercial area. Taking these factors into consideration, this study deemed the most plausible and rational factor for determining store similarity using Company E's current data to be the sales trend of products within stores. This information reflects customer purchasing behavior and commercial area attributes. However, comparing the sales trend of each SKU across all stores to determine similarity would be inefficient, as it would necessitate processing a sales quantity matrix of over 10,000 SKUs for approximately 6000 stores. Consequently, product categories representing individual SKU attributes and sales time slots were chosen as pivotal factors for determining similarity. Thus, store similarity was calculated based on the sales proportion of product categories and sales time slots among all products. **Construction of Input Variable Matrix** To perform similarity calculations, we constructed an input variable matrix based on Store x category proportions and Store x time slot sales proportions. In this context, categories represent sets of SKUs with similar attributes, and, specifically, we composed the input variable matrix for similarity calculation using 180 intermediate categories derived from Company E's product classification rules. **Measuring Store Similarity:** In collaborative filtering recommendation techniques, user similarity is utilized to select neighbors for the target user. Various metrics such as Euclidean distance, Pearson correlation, cosine similarity, and mean squared differences are employed for calculating similarity [17]. In this study, users were defined as stores, and store similarity was computed based on category-wise revenue proportions. Furthermore, we utilized cosine similarity as there was no bias in the measurements of the revenue figures for the 180 categories used in the similarity calculation. **Calculating Product Recommendation Scores:** Typically, a product recommendation model selects the top N items that are expected to be most preferred by the target customer and presents them as a list of recommended products [18]. In traditional movie or  
Figure 2: Recommendation model. document recommendations, preference can be predicted using evaluation scores such as satisfaction scores or preference scores provided by customers [19]. However, in this study, we employed a user-based top-N item recommendation technique [20]. The calculation of recommendation scores for products involved the following steps: First, based on the similarity matrix calculated from store data, we selected the top 30 most similar stores. Next, from these stores, we identified the 500 products with the highest daily sales volume. Finally, we computed the recommendation score by taking the weighted average of the store similarity scores and the sales frequency of each product. The reason for using sales frequency as a weight was to prioritize recommending products with higher sales.