diff --git a/server/website/website/tasks/async_tasks.py b/server/website/website/tasks/async_tasks.py
index 8900fd6..d18c9c0 100644
--- a/server/website/website/tasks/async_tasks.py
+++ b/server/website/website/tasks/async_tasks.py
@@ -21,7 +21,7 @@ from analysis.constraints import ParamConstraintHelper
 from website.models import (PipelineData, PipelineRun, Result, Workload, KnobCatalog,
                             MetricCatalog, SessionKnob)
 from website.parser import Parser
-from website.types import PipelineTaskType
+from website.types import PipelineTaskType, AlgorithmType
 from website.utils import DataUtil, JSONUtil
 from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG  # pylint: disable=no-name-in-module
 from website.settings import (DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE,
@@ -543,9 +543,8 @@ def configuration_recommendation(recommendation_input):
 
     session = newest_result.session
     res = None
-    assert algorithm in ['gpr', 'dnn']
 
-    if algorithm == 'dnn':
+    if algorithm == AlgorithmType.DNN:
         # neural network model
         model_nn = NeuralNet(n_input=X_samples.shape[1],
                              batch_size=X_samples.shape[0],
@@ -562,7 +561,7 @@ def configuration_recommendation(recommendation_input):
         session.dnn_model = model_nn.get_weights_bin()
         session.save()
 
-    elif algorithm == 'gpr':
+    elif algorithm == AlgorithmType.OTTERTUNE:
         # default gpr model
         model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
                       magnitude=DEFAULT_MAGNITUDE,
diff --git a/server/website/website/views.py b/server/website/website/views.py
index e9b618b..a29fb4a 100644
--- a/server/website/website/views.py
+++ b/server/website/website/views.py
@@ -529,14 +529,14 @@ def handle_result_files(session, files):
     result_id = result.pk
     response = None
     if session.algorithm == AlgorithmType.OTTERTUNE:
-        response = chain(aggregate_target_results.s(result.pk),
+        response = chain(aggregate_target_results.s(result.pk, session.algorithm),
                          map_workload.s(),
                          configuration_recommendation.s()).apply_async()
     elif session.algorithm == AlgorithmType.DDPG:
         response = chain(train_ddpg.s(result.pk),
                          configuration_recommendation_ddpg.s()).apply_async()
     elif session.algorithm == AlgorithmType.DNN:
-        response = chain(aggregate_target_results.s(result.pk, 'dnn'),
+        response = chain(aggregate_target_results.s(result.pk, session.algorithm),
                          map_workload.s(),
                          configuration_recommendation.s()).apply_async()