diff --git a/CHANGELOG.md b/CHANGELOG.md
index 38dc6b7a63..ff40730540 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- MRR metrics calculation ([#886](https://github.com/catalyst-team/catalyst/pull/886))
 - docs for MetricCallbacks ([#947](https://github.com/catalyst-team/catalyst/pull/947)) 
 - SoftMax, CosFace, ArcFace layers to contrib ([#939](https://github.com/catalyst-team/catalyst/pull/939))
 
@@ -90,7 +91,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [20.08] - 2020-08-09
 
 ### Added
-- Full metric learning pipeline including training and validation stages ([#886](https://github.com/catalyst-team/catalyst/pull/876))
 - `CMCScoreCallback` ([#880](https://github.com/catalyst-team/catalyst/pull/880))
 - kornia augmentations `BatchTransformCallback` ([#862](https://github.com/catalyst-team/catalyst/issues/862))
 - `average_precision` and `mean_average_precision` metrics ([#883](https://github.com/catalyst-team/catalyst/pull/883))
diff --git a/catalyst/dl/callbacks/metrics/__init__.py b/catalyst/dl/callbacks/metrics/__init__.py
index fd61543a97..89ddc35268 100644
--- a/catalyst/dl/callbacks/metrics/__init__.py
+++ b/catalyst/dl/callbacks/metrics/__init__.py
@@ -18,6 +18,7 @@
     IouCallback,
     JaccardCallback,
 )
+from catalyst.dl.callbacks.metrics.mrr import MRRCallback
 from catalyst.dl.callbacks.metrics.ppv_tpr_f1 import (
     PrecisionRecallF1ScoreCallback,
 )
diff --git a/catalyst/dl/callbacks/metrics/mrr.py b/catalyst/dl/callbacks/metrics/mrr.py
new file mode 100644
index 0000000000..29ea0c003b
--- /dev/null
+++ b/catalyst/dl/callbacks/metrics/mrr.py
@@ -0,0 +1,34 @@
+from catalyst.core import MetricCallback
+from catalyst.utils import metrics
+
+
+class MRRCallback(MetricCallback):
+    """Calculates the AUC  per class for each loader.
+
+    .. note::
+        Currently, supports binary and multi-label cases.
+    """
+
+    def __init__(
+        self,
+        input_key: str = "targets",
+        output_key: str = "logits",
+        prefix: str = "mrr",
+    ):
+        """
+        Args:
+            input_key (str): input key to use for mrr calculation
+                specifies our ``y_true``
+            output_key (str): output key to use for mrr calculation;
+                specifies our ``y_pred``
+            prefix (str): name to display for mrr when printing
+        """
+        super().__init__(
+            prefix=prefix,
+            metric_fn=metrics.mrr,
+            input_key=input_key,
+            output_key=output_key,
+        )
+
+
+__all__ = ["MRRCallback"]
diff --git a/catalyst/dl/callbacks/metrics/tests/__init__.py b/catalyst/dl/callbacks/metrics/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/catalyst/metrics/__init__.py b/catalyst/metrics/__init__.py
index e69de29bb2..24f7b086b3 100644
--- a/catalyst/metrics/__init__.py
+++ b/catalyst/metrics/__init__.py
@@ -0,0 +1,2 @@
+# flake8: noqa
+from catalyst.metrics.mrr import mrr
diff --git a/catalyst/metrics/mrr.py b/catalyst/metrics/mrr.py
new file mode 100644
index 0000000000..cce322e27b
--- /dev/null
+++ b/catalyst/metrics/mrr.py
@@ -0,0 +1,53 @@
+"""
+MRR metric.
+"""
+
+import torch
+
+
+def mrr(outputs: torch.Tensor, targets: torch.Tensor, k=100) -> torch.Tensor:
+    """
+    Calculate the Mean Reciprocal Rank (MRR)
+    score given model ouptputs and targets
+    User's data aggreagtesd in batches.
+
+    The MRR@k is the mean overall user of the
+    reciprocal rank, that is the rank of the highest
+    ranked relevant item, if any in the top *k*, 0 otherwise.
+    https://en.wikipedia.org/wiki/Mean_reciprocal_rank
+
+    Args:
+        outputs (torch.Tensor):
+            Tensor weith predicted score
+            size: [batch_size, slate_length]
+            model outputs, logits
+        targets (torch.Tensor):
+            Binary tensor with ground truth.
+            1 means the item is relevant
+            for the user and 0 not relevant
+            size: [batch_szie, slate_length]
+            ground truth, labels
+        k (int):
+            Parameter fro evaluation on top-k items
+
+    Returns:
+        result (torch.Tensor):
+            The mrr score for each user.
+    """
+    k = min(outputs.size(1), k)
+    _, indices_for_sort = outputs.sort(descending=True, dim=-1)
+    true_sorted_by_preds = torch.gather(
+        targets, dim=-1, index=indices_for_sort
+    )
+    true_sorted_by_pred_shrink = true_sorted_by_preds[:, :k]
+
+    values, indices = torch.max(true_sorted_by_pred_shrink, dim=1)
+    indices = indices.type_as(values).unsqueeze(dim=0).t()
+    result = torch.tensor(1.0) / (indices + torch.tensor(1.0))
+
+    zero_sum_mask = values == 0.0
+    result[zero_sum_mask] = 0.0
+    return result
+
+
+__all__ = ["mrr"]
diff --git a/catalyst/metrics/tests/__init__.py b/catalyst/metrics/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/catalyst/metrics/tests/test_mrr.py b/catalyst/metrics/tests/test_mrr.py
new file mode 100644
index 0000000000..8e8bd800ab
--- /dev/null
+++ b/catalyst/metrics/tests/test_mrr.py
@@ -0,0 +1,71 @@
+import torch
+
+from catalyst import metrics
+
+
+def test_mrr():
+    """
+    Tests for catalyst.metrics.mrr metric.
+    """
+    # # check 0 simple case
+    y_pred = [0.5, 0.2]
+    y_true = [1.0, 0.0]
+
+    mrr = metrics.mrr(torch.Tensor([y_pred]), torch.Tensor([y_true]))
+    assert mrr[0][0] == 1
+
+    # check 1 simple case
+    y_pred = [0.5, 0.2]
+    y_true = [0.0, 1.0]
+
+    mrr = metrics.mrr(torch.Tensor([y_pred]), torch.Tensor([y_true]))
+    # mrr = metrics.mrr(torch.Tensor(y_pred), torch.Tensor(y_true))
+    assert mrr[0][0] == 0.5
+    # assert mrr == 0.5
+
+    # check 2 simple case
+    y_pred = [0.2, 0.5]
+    y_true = [0.0, 1.0]
+
+    mrr = metrics.mrr(torch.Tensor([y_pred]), torch.Tensor([y_true]))
+    assert mrr[0][0] == 1.0
+
+    # check 3 test multiple users
+    y_pred1 = [0.2, 0.5]
+    y_pred05 = [0.5, 0.2]
+    y_true = [0.0, 1.0]
+
+    mrr = metrics.mrr(
+        torch.Tensor([y_pred1, y_pred05]), torch.Tensor([y_true, y_true])
+    )
+    assert mrr[0][0] == 1.0
+    assert mrr[1][0] == 0.5
+
+    # check 4 test with k
+    y_pred1 = [4.0, 2.0, 3.0, 1.0]
+    y_pred2 = [1.0, 2.0, 3.0, 4.0]
+    y_true1 = [0, 0, 1.0, 1.0]
+    y_true2 = [0, 0, 1.0, 1.0]
+
+    y_pred_torch = torch.Tensor([y_pred1, y_pred2])
+    y_true_torch = torch.Tensor([y_true1, y_true2])
+
+    mrr = metrics.mrr(y_pred_torch, y_true_torch, k=3)
+
+    assert mrr[0][0] == 0.5
+    assert mrr[1][0] == 1.0
+
+    # check 5 test with k
+
+    y_pred1 = [4.0, 2.0, 3.0, 1.0]
+    y_pred2 = [1.0, 2.0, 3.0, 4.0]
+    y_true1 = [0, 0, 1.0, 1.0]
+    y_true2 = [0, 0, 1.0, 1.0]
+
+    y_pred_torch = torch.Tensor([y_pred1, y_pred2])
+    y_true_torch = torch.Tensor([y_true1, y_true2])
+
+    mrr = metrics.mrr(y_pred_torch, y_true_torch, k=1)
+
+    assert mrr[0][0] == 0.0
+    assert mrr[1][0] == 1.0
diff --git a/catalyst/utils/metrics/__init__.py b/catalyst/utils/metrics/__init__.py
index 2211d0dbc0..49cd570bcb 100644
--- a/catalyst/utils/metrics/__init__.py
+++ b/catalyst/utils/metrics/__init__.py
@@ -3,6 +3,7 @@
     accuracy,
     multi_label_accuracy,
 )
+
 from catalyst.utils.metrics.auc import auc
 from catalyst.utils.metrics.cmc_score import cmc_score_count, cmc_score
 from catalyst.utils.metrics.dice import dice, calculate_dice
diff --git a/docs/api/utils.rst b/docs/api/utils.rst
index 561f5fbaae..4cfe66902a 100644
--- a/docs/api/utils.rst
+++ b/docs/api/utils.rst
@@ -290,3 +290,10 @@ Functional
     :members:
     :undoc-members:
     :show-inheritance:
+
+MRR
+~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: catalyst.metrics.mrr
+    :members:
+    :undoc-members:
+    :show-inheritance: