diff --git a/cornac/models/comparer/recom_comparer_obj.pyx b/cornac/models/comparer/recom_comparer_obj.pyx
index 1c522b6f..3c2162a2 100644
--- a/cornac/models/comparer/recom_comparer_obj.pyx
+++ b/cornac/models/comparer/recom_comparer_obj.pyx
@@ -663,7 +663,7 @@ class ComparERObj(Recommender):
             item_score = self.U2[item_id, :].dot(self.U1[user_id, :]) + self.H2[item_id, :].dot(self.H1[user_id, :])
             return item_score
 
-    def rank(self, user_id, item_ids=None):
+    def rank(self, user_id, item_ids=None, k=None):
         """Rank all test items for a given user.
 
         Parameters
diff --git a/cornac/models/comparer/recom_comparer_sub.pyx b/cornac/models/comparer/recom_comparer_sub.pyx
index e1eec1c7..69dad58e 100644
--- a/cornac/models/comparer/recom_comparer_sub.pyx
+++ b/cornac/models/comparer/recom_comparer_sub.pyx
@@ -759,7 +759,7 @@ class ComparERSub(MTER):
 
         return correct, skipped, loss, bpr_loss
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, k=None):
         if self.alpha > 0 and self.n_top_aspects > 0:
             n_top_aspects = min(self.n_top_aspects, self.num_aspects)
             ts1 = np.einsum("abc,a->bc", self.G1, self.U[user_idx])
diff --git a/cornac/models/efm/recom_efm.pyx b/cornac/models/efm/recom_efm.pyx
index 5d6dd582..b0dc140e 100644
--- a/cornac/models/efm/recom_efm.pyx
+++ b/cornac/models/efm/recom_efm.pyx
@@ -468,7 +468,7 @@ class EFM(Recommender):
             item_score = self.U2[item_idx, :].dot(self.U1[user_idx, :]) + self.H2[item_idx, :].dot(self.H1[user_idx, :])
             return item_score
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, k=None):
         """Rank all test items for a given user.
 
         Parameters
diff --git a/cornac/models/lrppm/recom_lrppm.pyx b/cornac/models/lrppm/recom_lrppm.pyx
index 2c8ec547..3a2c9884 100644
--- a/cornac/models/lrppm/recom_lrppm.pyx
+++ b/cornac/models/lrppm/recom_lrppm.pyx
@@ -516,7 +516,7 @@ class LRPPM(Recommender):
             item_score = self.I[i_idx].dot(self.U[u_idx])
             return item_score
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, k=None):
         if self.alpha > 0 and self.num_top_aspects > 0:
             n_items = self.num_items
             num_top_aspects = min(self.num_top_aspects, self.num_aspects)