diff --git a/docs/source/get_started/quick_start.rst b/docs/source/get_started/quick_start.rst
index 07075af72..ebbd0b8bc 100644
--- a/docs/source/get_started/quick_start.rst
+++ b/docs/source/get_started/quick_start.rst
@@ -11,7 +11,7 @@ Quick-start From API
 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 Before running a model, firstly you need to prepare and load data. To help users quickly get start, 
 RecBole has a build-in dataset **ml-100k** and you can directly use it. However, if you want to use other datasets, you can read
-:doc:`../usage/running_new_dataset` for more information.
+:doc:`../user_guide/usage/running_new_dataset` for more information.
 
 Then, you need to set data config for data loading. You can create a `yaml` file called `test.yaml` and write the following settings:
 
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 851b8a8cb..87da0150a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -4,7 +4,7 @@
 
 =========================================================
 
-`HomePage <https://recbole.io/>`_ | `Docs <https://recbole.io/docs/>`_ | `GitHub <https://github.com/RUCAIBox/RecBole>`_ | `Datasets <https://github.com/RUCAIBox/RecDatasets>`_ | `v0.1.2 </docs/v0.1.2/>`_
+`HomePage <https://recbole.io/>`_ | `Docs <https://recbole.io/docs/>`_ | `GitHub <https://github.com/RUCAIBox/RecBole>`_ | `Datasets <https://github.com/RUCAIBox/RecDatasets>`_ | `v0.1.2 </docs/v0.1.2/>`_ | `v0.2.0 </docs/v0.2.0/>`_
 
 Introduction
 -------------------------
diff --git a/recbole/utils/case_study.py b/recbole/utils/case_study.py
index 5430f5cb1..3775b4a3e 100644
--- a/recbole/utils/case_study.py
+++ b/recbole/utils/case_study.py
@@ -15,6 +15,8 @@
 import numpy as np
 import torch
 
+from recbole.data.interaction import Interaction
+
 
 @torch.no_grad()
 def full_sort_scores(uid_series, model, test_data, device=None):
@@ -34,20 +36,19 @@ def full_sort_scores(uid_series, model, test_data, device=None):
         torch.Tensor: the scores of all items for each user in uid_series.
     """
     device = device or torch.device('cpu')
-    uid_series = np.array(uid_series)
+    uid_series = torch.tensor(uid_series)
     uid_field = test_data.dataset.uid_field
     dataset = test_data.dataset
     model.eval()
 
     if not test_data.is_sequential:
-        index = np.isin(test_data.user_df[uid_field].numpy(), uid_series)
-        input_interaction = test_data.user_df[index]
+        input_interaction = dataset.join(Interaction({uid_field: uid_series}))
         history_item = test_data.uid2history_item[uid_series]
         history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)])
         history_col = torch.cat(list(history_item))
         history_index = history_row, history_col
     else:
-        index = np.isin(dataset[uid_field].numpy(), uid_series)
+        _, index = (dataset[uid_field] == uid_series[:, None]).nonzero(as_tuple=True)
         input_interaction = dataset[index]
         history_index = None