## 4 Experiments
### Detailed Comparison  
Performance Analysis with Concurrent Purchase  
The proposed method that reflects concurrent purchases, \"BERT With\", significantly outperforms the models that utilize the tabular representation with a concurrent purchase flag, namely \"XGBoost With\" and \"DeepFM With\". The proposed method surpasses the benchmark models in classification performance metrics with accuracy and F1 score. It is noteworthy that NDCG@5 and NDCG@10, practical metrics for evaluating recommendation systems, are significantly higher for the proposed method, with values of 0.4910 and 0.5376, respectively, compared to the benchmark models. This demonstrates that the proposed methodology effectively models user history by thoroughly describing orders, including concurrent purchases, as natural language.  
Performance Analysis without Considering Concurrent Purchase  
To analyze the performance changes when concurrent purchase information is excluded, we trained XGBoost and DeepFM using the tabular dataset variant without concurrent purchase flags. For the BERT model, we adapted the input by representing each order as a sequential list of single-product purchases, disregarding the nuances of concurrent purchases. The models trained under these constraints, \"XGBoost Without\", \"DeepFM Without\", and \"BERT Without\", all show decreased performance compared to the models that consider concurrent purchases. It is noteworthy that the best-performing proposed method, \"BERT With\", achieved a performance improvement of more than 0.0287 in NDCG@5 and 0.0390 in NDCG@10 compared to \"BERT Without\". This proves that the simple idea of describing order history in natural language can greatly improve recommendation performance. The enhanced sequence modeling framework of the transformer architecture further contributes to this improvement.  
Comparison with the Pretrained BERT  
The pretrained BERT model is an excellent model that has learned from a large corpus on its own. However, we conducted an additional analysis to examine the distinctiveness of the proposed method that fine-tunes BERT on the recommendation data. We applied the pretrained BERT without fine-tuning to predict the same concurrent purchase representations as \"BERT With\". The performance is shown in the \"BERT (pretrained)\" row of Table 3, which falls significantly short of the proposed method. This confirms that fine-tuning BERT  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & **NDCG@5** & **NDCG@10** \\ \hline XGBoost\({}_{\text{with}}\) & 0.7461 & 0.7356 & 0.3815 & 0.4333 \\ XGBoost\({}_{\text{without}}\) & 0.7434 & 0.7320 & 0.3781 & 0.4330 \\ DeepFM\({}_{\text{with}}\) & 0.7308 & 0.7464 & 0.2767 & 0.3423 \\ DeepFM\({}_{\text{without}}\) & 0.7254 & 0.7348 & 0.2742 & 0.3309 \\ BERT\({}_{\text{with}}\) & **0.7960** & **0.7960** & **0.4910** & **0.5376** \\ BERT\({}_{\text{without}}\) & 0.7720 & 0.7718 & 0.4623 & 0.5037 \\ BERT\({}_{\text{pretrained}}\) & 0.4808 & 0.4271 & 0.0487 & 0.0695 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of recommendation models with and without concurrent purchase information on _[PERSON]’s_ dataset. The best value in each column is highlighted in bold.  
on recommendation data, as employed in our methodology, is highly effective in modeling recommendation predictions.  
Comparison of BERT for Next Sentence Prediction and BERT for Sequence Classification  
Additionally, using BERT For Next Sentence Prediction yielded better results compared to using BERT For sequence classification. This supports our assumption in Section 3.1 that BERT's Next Sentence Prediction is more suited to the nature of e-commerce problems, demonstrating its superior performance in this context.