diff --git a/recommenders/models/wide_deep/wide_deep_utils.py b/recommenders/models/wide_deep/wide_deep_utils.py
index adc0fdc9d..ba7cbefe6 100644
--- a/recommenders/models/wide_deep/wide_deep_utils.py
+++ b/recommenders/models/wide_deep/wide_deep_utils.py
@@ -116,6 +116,9 @@ def __init__(
     ):
         self._check_cols_df('ratings', ratings, [user_col, item_col, rating_col])
         self._check_cols_df('item_feat', item_feat, [item_col])
+        self._check_cols_df('user_feat', user_feat, [user_col])
+        self._check_cont_features('item_feat', item_feat, item_col)
+        self._check_cont_features('user_feat', user_feat, user_col)
 
         self.user_col = user_col
         self.item_col = item_col
@@ -123,7 +126,7 @@ def __init__(
         self.ratings = ratings.copy()
         self.item_feat = item_feat.set_index(item_col).copy() if item_feat is not None else pd.DataFrame()
         self.user_feat = user_feat.set_index(user_col).copy() if user_feat is not None else pd.DataFrame()
-        self.n_cont_features = n_cont_features or len(self._get_continuous_features(self.item_feat.index.min(), self.user_feat.index.min()))
+        self.n_cont_features = n_cont_features or self._get_continuous_features([self.item_feat.index.min()], [self.user_feat.index.min()]).shape[1]
 
         self.n_users = n_users or ratings[user_col].max()+1
         self.n_items = n_items or ratings[item_col].max()+1
@@ -140,23 +143,42 @@ def _check_cols_df(df_name: str, df: Optional[pd.DataFrame], cols: list[str]) ->
                 raise ValueError(f"Column '{c}' is not present on {df_name}")
 
         return True
+
+    @staticmethod
+    def _check_cont_features(df_name: str, df: Optional[pd.DataFrame], col: str) -> bool:
+        if df is None or df.empty:
+            return True
+
+        for c in df.columns:
+            if c == col:
+                continue
+            
+            # Check that dtype is float
+            if not pd.api.types.is_numeric_dtype(df[c].dtype):
+                raise ValueError(f"Column '{c}' from {df_name} has dtype {df[c].dtype}. Only numeric dtypes are allowed.")
     
     def __len__(self):
         return len(self.ratings)
 
-    def _get_continuous_features(self, item_id, user_id) -> np.array:
+    def _get_continuous_features(self, item_ids, user_ids) -> np.array:
+        if len(item_ids) != len(user_ids):
+            raise ValueError('item_ids and user_ids should have same length')
+
+        if self.item_feat.empty and self.user_feat.empty:
+            return np.array([])
+        
         # Put empty array so concat doesn't fail
-        continuous_features = [np.array([])]
+        continuous_features = []
 
         if not self.item_feat.empty:
-            feats = self.item_feat.loc[item_id]
-            continuous_features.extend(np.array(f).reshape(-1) for f in feats)
+            feats = self.item_feat.loc[item_ids]
+            continuous_features.extend(feats.values)
 
         if not self.user_feat.empty:
-            feats = self.user_feat.loc[user_id]
-            continuous_features.extend(np.array(f).reshape(-1) for f in feats)
+            feats = self.user_feat.loc[user_ids]
+            continuous_features.extend(feats.values)
 
-        return np.concatenate(continuous_features).astype('float32')
+        return np.stack(continuous_features)
 
     def __getitem__(self, idx):
         # TODO: Get additional embeddings too (e.g: user demographics)
@@ -167,7 +189,8 @@ def __getitem__(self, idx):
         }
 
         if self.n_cont_features:
-            ret['continuous_features'] = self._get_continuous_features(item[self.item_col], item[self.user_col])
+            # Reshape because it is only one item
+            ret['continuous_features'] = self._get_continuous_features([item[self.item_col]], [item[self.user_col]]).reshape(-1)
 
         return ret, self.ratings[self.rating_col].iloc[idx]
     
@@ -350,14 +373,15 @@ def _get_uip_cont(self, user_ids, item_ids, remove_seen: bool):
                 self.train.ratings.set_index([self.user_col, self.item_col]).index
             )
 
+        uip = uip.to_frame(index=False)
+
         cont_features = None
-        # TODO: [!] CACHE THE "RANKING POOL" (uip and cont_features) IT TAKES SEVERAL SECONDS TO GEN
         if self.train.n_cont_features > 0:
             cont_features = torch.from_numpy(
-                np.stack(uip.map(lambda x: self.train._get_continuous_features(*x)).values)
+                self.train._get_continuous_features(uip.values[:,0], uip.values[:,1])
             )
 
-        return uip.to_frame(index=False), cont_features
+        return uip, cont_features
 
     def recommend_k_items(
         self, user_ids=None, item_ids=None, top_k=10, remove_seen=True,