diff --git a/libreco/algorithms/swing.py b/libreco/algorithms/swing.py
index 7c9430df..1beae679 100644
--- a/libreco/algorithms/swing.py
+++ b/libreco/algorithms/swing.py
@@ -79,19 +79,26 @@ def fit(
         self.show_start_time()
         user_interacts = build_sparse(train_data.sparse_interaction)
         item_interacts = build_sparse(train_data.sparse_interaction, transpose=True)
-        self.rs_model = recfarm.Swing(
-            self.top_k,
-            self.alpha,
-            self.max_cache_num,
-            self.n_users,
-            self.n_items,
-            user_interacts,
-            item_interacts,
-            self.user_consumed,
-            self.default_pred,
-        )
-        with time_block("swing computing", verbose=1):
-            self.rs_model.compute_swing(self.num_threads, self.incremental)
+        if self.incremental:
+            assert self.rs_model is not None
+            with time_block("update swing", verbose=1):
+                self.rs_model.update_swing(
+                    self.num_threads, user_interacts, item_interacts
+                )
+        else:
+            self.rs_model = recfarm.Swing(
+                self.top_k,
+                self.alpha,
+                self.max_cache_num,
+                self.n_users,
+                self.n_items,
+                user_interacts,
+                item_interacts,
+                self.user_consumed,
+                self.default_pred,
+            )
+            with time_block("swing computing", verbose=1):
+                self.rs_model.compute_swing(self.num_threads)
 
         num = self.rs_model.num_swing_elements()
         density_ratio = 100 * num / (self.n_items * self.n_items)
@@ -137,17 +144,21 @@ def recommend_user(
                 result_recs[u] = popular_recommendations(
                     self.data_info, inner_id, n_rec
                 )
+
         if user_ids:
-            computed_recs, no_rec_indices = self.rs_model.recommend(
+            computed_recs, additional_rec_counts = self.rs_model.recommend(
                 user_ids,
                 n_rec,
                 filter_consumed,
                 random_rec,
             )
-            for i in no_rec_indices:
-                computed_recs[i] = popular_recommendations(
-                    self.data_info, inner_id=True, n_rec=n_rec
-                )
+            for rec, arc in zip(computed_recs, additional_rec_counts):
+                if arc > 0:
+                    additional_recs = popular_recommendations(
+                        self.data_info, inner_id=True, n_rec=arc
+                    )
+                    rec.extend(additional_recs)
+
             user_recs = construct_rec(self.data_info, user_ids, computed_recs, inner_id)
             result_recs.update(user_recs)
         return result_recs