## 1 Introduction  
Recently, the retail industry has been collecting various traces of customers' shopping journeys to provide personalized recommendations based on these data. Furthermore, efforts have been made to develop more sophisticated personalized recommendations to not only attract customers to the shopping space but also enhance purchases within that space. However, these efforts have primarily focused on the online e-commerce domain, and personalized product recommendations for offline stores have proven challenging despite various attempts. To achieve customized product recommendations for offline customers, it is crucial to gather basic customer information and purchase journey data. Furthermore, accurately identifying the customer's current location and providing appropriate products in a timely manner are essential. In the mid-2010s, some companies with high brand loyalty attempted offline target marketing using location-based systems (LBS) such as beacons and mobile coupon distribution. However, due to technological constraints, customer resistance, and challenges related to return on investment (ROI), these systems had limitations in terms of effectiveness. Especially for companies operating multiple stores such as convenience stores, collecting customer information is difficult due to the diverse range of visitors. While investing in systems and infrastructure for collecting customer information and target marketing is possible, actual instances of such investments are rare, and even when investments are made, there are few success stories resulting in increased sales and profits. In this study, we aimed to find a solution by shifting the approach from targeting individual customers to targeting stores in the existing personalized product recommendation models. Traditional personalized recommendation models recommend products with a high likelihood of purchase or selection based on customer purchase/selection history, preferences, and evaluations. In this study, a model was developed to recommend products based on individual store similarity. Store similarity was calculated based on each store's category-specific sales history. This store-based model predicts products that customers visiting a particular store would want to purchase, even if those products are not currently stocked in that store. These recommendations were validated based on actual sales generated by the store. The contribution of this study lies in the expansion of personalized recommendation research, primarily conducted on online platforms, to offline businesses such as convenience stores. While recommendation system algorithms continue to evolve and diversify, limitations still exist in terms of data analysis and algorithms. Moreover, successful implementation in real business scenarios requires additional domain-specific rules and logic based on the fundamental recommendation algorithms. In this study, a store-specific product recommendation model based on convenience store product and sales data was developed. This model was then integrated with domain-specific rules and logic to validate its effectiveness. In South Korea, there are approximately 50,000 convenience stores, with over 95% of them operated through individual franchise agreements with the headquarters. Generally, each store carries around 2500 to 3500 SKU products, with some variation depending on the store's location and size among the approximately 20,000 products offered by the company. When a new convenience store opens, Company E selects products based on pre-defined categories for commercial regions. However, this simplistic approach of categorizing based solely on store location does not fully capture the unique characteristics of each individual store. Additionally, after the initial product selection, store owners must decide which new products to introduce from a pool of around 100 different products each month. They usually rely on recommendations from their surroundings or basic product information provided by the company.