## 2 Literature Review  
As personal data have rapidly increased and become more accessible, recommendation systems are widely applied across various online platforms. Especially for online shopping malls, movie and music content providers, news websites, bookstores, and research-related sites, recommendation systems are essential. From the perspective of sellers/suppliers, providing appropriate recommendations that induce actual purchases has a direct impact on revenue generation, underscoring the importance of accurate product recommendations. Consequently, research to enhance the accuracy and efficiency of recommendation systems has been actively pursued [1].  
The concept of recommendation systems was initially proposed by [PERSON] in 1990 [2]. In 1994, the University of Minnesota Group Lens research team developed the first automated recommendation system \"Group Lens\" using the UCF algorithm. In 1997, content-based collaborative filtering (CF) algorithms were introduced for information retrieval [3]. In 1998, an approach using collaborative filtering with singular value decomposition for classification tasks was introduced [4]. In 2002, item-based CF algorithms were proposed [5], and [PERSON] et al. [6] established the widely known Item-to-Item CF method used in Amazon's recommendation system. [PERSON] and [PERSON] [7] increased the accuracy of recommendation systems using implicit user interaction data. [PERSON] et al. [8] presenteda recommendation algorithm utilizing multi-user similarity. [PERSON] et al. [9] proposed an improved user-similarity-based algorithm for collaborative filtering. Since 2015, deep neural networks have been applied to enhance recommendations in large-scale content. In 2016, Google introduced the high-performance YoutubeDNN model by integrating classic recommendation system architectures [10]. Subsequent developments include VAE-CF considering user preferences [11], RNN-based recommendation systems considering user preference changes [11], and the RKSA model using the Transformer architecture to consider item relationships [12]. Recent research in recommendation systems has primarily focused on improving algorithm accuracy, with online customers being the main target [13; 14; 15].  
This study differs from previous research in several ways. It implements and validates a recommendation system using user-based collaborative filtering based on the hypothesis that if a specific store prefers certain products, similar preferences may be present in other stores. While prior studies mainly focused on recommending products to individual online customers, this study aims to recommend products to offline stores. Therefore, unlike previous research that emphasized enhancing the efficiency and accuracy of recommendation algorithms, this study focuses on effectively implementing a recommendation system based on changes in the recommendation target.  
In a similar context, [PERSON] and [PERSON] [16] implemented and validated a recommendation system for SKU recommendations in offline fashion stores of the same brand. Their study proposed methods for handling SKU recommendations at the store level for distribution companies dealing with the same brand across different countries and regions using collaborative and hybrid filtering. Their models, based on sales data from 52 stores, 24 items, and 404 SKUs of the 'K' brand over a 3-week period, achieved recommendation precision of 9.9% for collaborative filtering and 10.8% for clustering-based recommendations.  
This study proposes and validates a recommendation model for offline stores similar to that of [PERSON] and [PERSON] [16] but with three key differences. Firstly, this study utilizes much more data, including sales data from over 6000 convenience stores for a year. Secondly, it considers various business characteristics of customer purchasing behavior and system implementation in similarity evaluation. Lastly, the recommendations are validated based on actual sales generated by the store.