# BERT4 Loc: BERT for Location--POI Recommender System
## 5 Results and Analysis
### Overall Results  
The comparison between our model and all the baselines is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Top-k**} & \multirow{2}{*}{ \begin{tabular}{c} **Precision** \\ **(Mean \(\pm\) SD)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Recall** \\ **(Mean \(\pm\) SD)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **F1-Score** \\ **(Mean \(\pm\) SD)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **HR** \\ **(Mean \(\pm\) SD)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **NDCG** \\ **(Mean \(\pm\) SD)** \\ \end{tabular}  } \\ \hline \multicolumn{7}{c}{**Yelp Dataset**} \\ \hline BERT4 Loc & \(10\) & \(0.56\pm 0.04\) & \(0.45\pm 0.05\) & \(0.50\pm 0.05\) & \(0.82\pm 0.06\) & \(0.42\pm 0.03\) \\  & \(20\) & \(0.52\pm 0.05\) & \(0.60\pm 0.07\) & \(0.56\pm 0.05  
Based on the results shown in Table 3, BERT4 Loc and BERT4 REC show the highest precision, recall, and F1-score among all models. These models demonstrate a strong performance across all top-k values (10, 20, and 50). BERT4 Loc achieves the highest HR and NDCG for all top-k values, indicating that it is highly effective in ranking relevant items for users. The comparison between BERT4 Rec and BERT4 Loc reveals that BERT4 Loc performs better in location-aware recommendation scenarios. BERT4 Rec is designed to take in user-item features without considering contextual features, and we consider the contextual information in BERT4 REC in this experiment; nevertheless, BERT4 Loc with its default design to leverage rich contextual features makes more accurate recommendations. In our experiments, we provide the same input features to all baseline models; a model's ability to naturally incorporate rich contextual features can give it an advantage in providing better recommendations. MultiVAE, RepeatNet, SASRecF, and ENMF models show relatively lower performance compared to BERT4 Loc and BERT4 REC. Their precision, recall, and F1-score values are lower, indicating that these models are less accurate in predicting the top-N items for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Top-k**} & **Precision** & **Recall** & **F1-Score** & **HR** & **NDCG** \\  & & **(Mean \(\pm\) SD)** & **(Mean \(\pm\) SD)** & **(Mean  
users. The HR and NDCG metrics also indicate that these models are less effective in ranking relevant items. SLIM, NCF, GRU4 Rec, and FPMC models show moderate performance in comparison to the other models. Their precision, recall, and F1-score values are better than MultiVAE, RepeatNet, SASRecF, and ENMF but lower than BERT4 Loc and BERT4 REC. The HR and NDCG metrics for these models are also intermediate, indicating that they have moderate effectiveness in ranking relevant items. The superiority of our BERT-based model is attributed to its design, which has the following distinctive attributes: (1) it considers the side information from the POI items in the item encoder; (2) it considers the contexts in the user model to better capture the sequential correlation of a user's POI history; and (3) it is based on sequential recommendation, which implicitly consider both the short- and long-term interests of users. Inherently, our model is based on the BERT architecture, which is a deep bidirectional self-attention model that can capture item relations on both sides, left and right, whereas other sequential recommendation models (in these experiments) only consider users' historical sequences from left to right. In real situations, a user's behavior depends on the user's current interests, which can evolve in a highly dynamic manner. Therefore, considering only previous items is insufficient in terms of accuracy. Our model can predict the next item by extracting user historical patterns based on the relationship between rated items in the history data of target users (item sequences).