diff --git a/recbole/model/sequential_recommender/bert4rec.py b/recbole/model/sequential_recommender/bert4rec.py
index 0a759d6ff..936e57d77 100644
--- a/recbole/model/sequential_recommender/bert4rec.py
+++ b/recbole/model/sequential_recommender/bert4rec.py
@@ -3,6 +3,11 @@
 # @Author  : Hui Wang
 # @Email   : hui.wang@ruc.edu.cn
 
+# UPDATE
+# @Time   : 2023/9/4
+# @Author : Enze Liu
+# @Email  : enzeeliu@foxmail.com
+
 r"""
 BERT4Rec
 ################################################
@@ -75,6 +80,10 @@ def __init__(self, config, dataset):
 
         self.LayerNorm = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
         self.dropout = nn.Dropout(self.hidden_dropout_prob)
+        self.output_ffn = nn.Linear(self.hidden_size, self.hidden_size)
+        self.output_gelu = nn.GELU()
+        self.output_ln = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
+        self.output_bias = nn.Parameter(torch.zeros(self.n_items))
 
         # we only need compute the loss at the masked position
         try:
@@ -124,7 +133,9 @@ def forward(self, item_seq):
         trm_output = self.trm_encoder(
             input_emb, extended_attention_mask, output_all_encoded_layers=True
         )
-        output = trm_output[-1]
+        ffn_output = self.output_ffn(trm_output[-1])
+        ffn_output = self.output_gelu(ffn_output)
+        output = self.output_ln(ffn_output)
         return output  # [B L H]
 
     def multi_hot_embed(self, masked_index, max_length):
@@ -172,8 +183,14 @@ def calculate_loss(self, interaction):
         if self.loss_type == "BPR":
             pos_items_emb = self.item_embedding(pos_items)  # [B mask_len H]
             neg_items_emb = self.item_embedding(neg_items)  # [B mask_len H]
-            pos_score = torch.sum(seq_output * pos_items_emb, dim=-1)  # [B mask_len]
-            neg_score = torch.sum(seq_output * neg_items_emb, dim=-1)  # [B mask_len]
+            pos_score = (
+                torch.sum(seq_output * pos_items_emb, dim=-1)
+                + self.output_bias[pos_items]
+            )  # [B mask_len]
+            neg_score = (
+                torch.sum(seq_output * neg_items_emb, dim=-1)
+                + self.output_bias[neg_items]
+            )  # [B mask_len]
             targets = (masked_index > 0).float()
             loss = -torch.sum(
                 torch.log(1e-14 + torch.sigmoid(pos_score - neg_score)) * targets
@@ -183,8 +200,9 @@ def calculate_loss(self, interaction):
         elif self.loss_type == "CE":
             loss_fct = nn.CrossEntropyLoss(reduction="none")
             test_item_emb = self.item_embedding.weight[: self.n_items]  # [item_num H]
-            logits = torch.matmul(
-                seq_output, test_item_emb.transpose(0, 1)
+            logits = (
+                torch.matmul(seq_output, test_item_emb.transpose(0, 1))
+                + self.output_bias
             )  # [B mask_len item_num]
             targets = (masked_index > 0).float().view(-1)  # [B*mask_len]
 
@@ -204,7 +222,9 @@ def predict(self, interaction):
         seq_output = self.forward(item_seq)
         seq_output = self.gather_indexes(seq_output, item_seq_len - 1)  # [B H]
         test_item_emb = self.item_embedding(test_item)
-        scores = torch.mul(seq_output, test_item_emb).sum(dim=1)  # [B]
+        scores = (torch.mul(seq_output, test_item_emb)).sum(dim=1) + self.output_bias[
+            test_item
+        ]  # [B]
         return scores
 
     def full_sort_predict(self, interaction):
@@ -216,7 +236,7 @@ def full_sort_predict(self, interaction):
         test_items_emb = self.item_embedding.weight[
             : self.n_items
         ]  # delete masked token
-        scores = torch.matmul(
-            seq_output, test_items_emb.transpose(0, 1)
+        scores = (
+            torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + self.output_bias
         )  # [B, item_num]
         return scores