diff --git a/recommenders/evaluation/python_evaluation.py b/recommenders/evaluation/python_evaluation.py
index a762fa10bd..3e972bc7e1 100644
--- a/recommenders/evaluation/python_evaluation.py
+++ b/recommenders/evaluation/python_evaluation.py
@@ -420,11 +420,11 @@ def precision_at_k(
     rating_pred,
     col_user=DEFAULT_USER_COL,
     col_item=DEFAULT_ITEM_COL,
-    col_rating=DEFAULT_RATING_COL,
     col_prediction=DEFAULT_PREDICTION_COL,
     relevancy_method="top_k",
     k=DEFAULT_K,
     threshold=DEFAULT_THRESHOLD,
+    **kwargs
 ):
     """Precision at K.
 
@@ -450,7 +450,7 @@ def precision_at_k(
     Returns:
         float: precision at k (min=0, max=1)
     """
-
+    col_rating = _get_rating_column(relevancy_method, **kwargs)
     df_hit, df_hit_count, n_users = merge_ranking_true_pred(
         rating_true=rating_true,
         rating_pred=rating_pred,
@@ -474,11 +474,11 @@ def recall_at_k(
     rating_pred,
     col_user=DEFAULT_USER_COL,
     col_item=DEFAULT_ITEM_COL,
-    col_rating=DEFAULT_RATING_COL,
     col_prediction=DEFAULT_PREDICTION_COL,
     relevancy_method="top_k",
     k=DEFAULT_K,
     threshold=DEFAULT_THRESHOLD,
+    **kwargs
 ):
     """Recall at K.
 
@@ -498,7 +498,7 @@ def recall_at_k(
         float: recall at k (min=0, max=1). The maximum value is 1 even when fewer than
         k items exist for a user in rating_true.
     """
-
+    col_rating = _get_rating_column(relevancy_method, **kwargs)
     df_hit, df_hit_count, n_users = merge_ranking_true_pred(
         rating_true=rating_true,
         rating_pred=rating_pred,
@@ -522,13 +522,13 @@ def ndcg_at_k(
     rating_pred,
     col_user=DEFAULT_USER_COL,
     col_item=DEFAULT_ITEM_COL,
-    col_rating=DEFAULT_RATING_COL,
     col_prediction=DEFAULT_PREDICTION_COL,
     relevancy_method="top_k",
     k=DEFAULT_K,
     threshold=DEFAULT_THRESHOLD,
     score_type="binary",
     discfun_type="loge",
+    **kwargs
 ):
     """Normalized Discounted Cumulative Gain (nDCG).
 
@@ -553,7 +553,7 @@ def ndcg_at_k(
     Returns:
         float: nDCG at k (min=0, max=1).
     """
-
+    col_rating = _get_rating_column(relevancy_method, **kwargs)
     df_hit, _, _ = merge_ranking_true_pred(
         rating_true=rating_true,
         rating_pred=rating_pred,
@@ -621,11 +621,11 @@ def map_at_k(
     rating_pred,
     col_user=DEFAULT_USER_COL,
     col_item=DEFAULT_ITEM_COL,
-    col_rating=DEFAULT_RATING_COL,
     col_prediction=DEFAULT_PREDICTION_COL,
     relevancy_method="top_k",
     k=DEFAULT_K,
     threshold=DEFAULT_THRESHOLD,
+    **kwargs
 ):
     """Mean Average Precision at k
 
@@ -657,7 +657,7 @@ def map_at_k(
     Returns:
         float: MAP at k (min=0, max=1).
     """
-
+    col_rating = _get_rating_column(relevancy_method, **kwargs)
     df_hit, df_hit_count, n_users = merge_ranking_true_pred(
         rating_true=rating_true,
         rating_pred=rating_pred,
@@ -736,6 +736,26 @@ def get_top_k_items(
 }
 
 
+def _get_rating_column(relevancy_method: str, **kwargs) -> str:
+    r"""Helper utility to simplify the arguments of eval metrics
+    Attemtps to address https://github.com/microsoft/recommenders/issues/1737.
+
+    Args:
+        relevancy_method (str): method for determining relevancy ['top_k', 'by_threshold', None]. None means that the
+            top k items are directly provided, so there is no need to compute the relevancy operation.
+
+    Returns:
+        str: rating column name.
+    """
+    if relevancy_method != "top_k":
+        if "col_rating" not in kwargs:
+            raise ValueError("Expected an argument `col_rating` but wasn't found.")
+        col_rating = kwargs.get("col_rating")
+    else:
+        col_rating = kwargs.get("col_rating", DEFAULT_RATING_COL)
+    return col_rating
+
+
 # diversity metrics
 def _check_column_dtypes_diversity_serendipity(func):
     """Checks columns of DataFrame inputs