## 1 Introduction  
In the burgeoning landscape of digital commerce, the evolution of consumer shopping behaviors has necessitated the advent of e-commerce platforms that not only streamline the shopping experience but also personalize it to cater to individual preferences. At the heart of this personalized shopping experience are sophisticated recommendation systems, meticulously engineered to decipher and predict user preferences, thereby suggesting products that align with their unique shopping patterns. A significant trend in consumer behavior is the occurrence of concurrent purchases, where multiple products are purchased together in a single order. This pattern differs from the sequential consumption behavior observed on video-sharing platforms like YouTube, where users typically engage with one video at a time. In the e-commerce domain, however, consumers often bundle various products in a single order to take advantage of benefits such as shipping discounts or special deals. This distinctive consumer behavior underscores the need for recommendation systems to account for the nuances of concurrent purchasing behaviors to enhance the shopping experience further. However, existing research on recommendation systems typically does not consider the complexities of concurrent buying behaviors. Existing studies approach the representation of user purchase history in two ways: as tabular datasets or as sequence datasets. Following the release of the transformer architecture, numerous studies have reported modeling purchase sequences and applying them to the recommendation field. However, there are few instances where these methods have been applied to recommendation systems that consider concurrent purchases. In existing research, purchase information is either aggregated into a tabular representation or simplified into a purchase sequence, ignoring the time differences between purchased products, thereby inherently disregarding concurrent purchase information. This simplification overlooks the complexity of consumer purchasing behaviors, where the simultaneous selection of multiple items offers valuable insights into user preferences and intentions. Consequently, this limitation significantly undermines the ability to accurately represent and utilize concurrent purchasing behaviors in recommendation systems, highlighting a critical gap in leveraging complex consumer purchasing patterns. Although there is a traditional recommendation problem known as next basket recommendation (NBR) [1; 2; 3; 4], it differs from concurrent purchases due to the difference in the number of items consumed simultaneously (added to the cart and purchased). Users often add more items to their cart than they actually purchase, and tend to buy only a subset of the items in the cart. Therefore, NBR algorithms often consider items added to the cart above a certain threshold as a signal during data preprocessing, while those below the threshold are treated as bias and removed. When applying NBR to purchase data, this results in excessive deletion of data that are actually a signal. For example, in the case of the representative NBR algorithm TIFUKNN [5], only transactions with four or more items in the cart are considered as signals, and those with fewer than four are treated as bias and removed during data preprocessing. Applying TIFUKNN to _[PERSON]_'s purchase data leaves only about 31% of the data, leading to the issue of misclassifying many data points as bias even though they are signals. Therefore, we considered concurrent purchases as a distinct problem separate from traditional NBR and conducted research to address it. This study aims to overcome the limitations of existing research by introducing a novel approach that integrates concurrent purchase data into e-commerce recommendation algorithms using a transformer-based model, namely BERT. BERT (Bidirectional Encoder Representations from Transformers) [6] is a pretrained language model that has shown remarkable performance in various natural language processing tasks.