diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py
index e3506f2e7..cc7acf8d3 100644
--- a/recbole/model/sequential_recommender/repeatnet.py
+++ b/recbole/model/sequential_recommender/repeatnet.py
@@ -248,11 +248,12 @@ def forward(self, all_memory, last_memory, item_seq, mask=None):
             output_er.masked_fill_(mask, -1e9)
 
         output_er = nn.Softmax(dim=-1)(output_er)
-        output_er = output_er.unsqueeze(1)
 
-        map_matrix = build_map(item_seq, self.device, max_index=self.num_item)
-        output_er = torch.matmul(output_er, map_matrix).squeeze(1).to(self.device)
-        repeat_recommendation_decoder = output_er.squeeze(1).to(self.device)
+        batch_size, b_len = item_seq.size()
+        repeat_recommendation_decoder = torch.zeros(
+            [batch_size, self.num_item], device=self.device
+        )
+        repeat_recommendation_decoder.scatter_add_(1, item_seq, output_er)
 
         return repeat_recommendation_decoder.to(self.device)
 
@@ -299,50 +300,12 @@ def forward(self, all_memory, last_memory, item_seq, mask=None):
         output_e = torch.cat([output_e, last_memory_values], dim=1)
         output_e = self.dropout(self.matrix_for_explore(output_e))
 
-        map_matrix = build_map(item_seq, self.device, max_index=self.num_item)
-        explore_mask = torch.bmm(
-            (item_seq > 0).float().unsqueeze(1), map_matrix
-        ).squeeze(1)
-        output_e = output_e.masked_fill(explore_mask.bool(), float("-inf"))
+        item_seq_first = item_seq[:, 0].unsqueeze(1).expand_as(item_seq)
+        item_seq_first = item_seq_first.masked_fill(item_seq > 0, 0)
+        item_seq_first.requires_grad_(False)
+        output_e.scatter_add_(
+            1, item_seq + item_seq_first, float("-inf") * torch.ones_like(item_seq)
+        )
         explore_recommendation_decoder = nn.Softmax(1)(output_e)
 
         return explore_recommendation_decoder
-
-
-def build_map(b_map, device, max_index=None):
-    """
-    project the b_map to the place where it in should be like this:
-        item_seq A: [3,4,5]   n_items: 6
-
-        after map: A
-
-        [0,0,1,0,0,0]
-
-        [0,0,0,1,0,0]
-
-        [0,0,0,0,1,0]
-
-        batch_size * seq_len ==>> batch_size * seq_len * n_item
-
-    use in RepeatNet:
-
-    [3,4,5] matmul [0,0,1,0,0,0]
-
-                   [0,0,0,1,0,0]
-
-                   [0,0,0,0,1,0]
-
-    ==>>> [0,0,3,4,5,0] it works in the RepeatNet when project the seq item into all items
-
-    batch_size * 1 * seq_len matmul batch_size * seq_len * n_item ==>> batch_size * 1 * n_item
-    """
-    batch_size, b_len = b_map.size()
-    if max_index is None:
-        max_index = b_map.max() + 1
-    if torch.cuda.is_available():
-        b_map_ = torch.FloatTensor(batch_size, b_len, max_index).fill_(0).to(device)
-    else:
-        b_map_ = torch.zeros(batch_size, b_len, max_index)
-    b_map_.scatter_(2, b_map.unsqueeze(2), 1.0)
-    b_map_.requires_grad = False
-    return b_map_