diff --git a/core/java/com/android/internal/app/ResolverComparator.java b/core/java/com/android/internal/app/ResolverComparator.java
index d9ab47e780d9b..096fcb83e755d 100644
--- a/core/java/com/android/internal/app/ResolverComparator.java
+++ b/core/java/com/android/internal/app/ResolverComparator.java
@@ -343,53 +343,42 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
     class LogisticRegressionAppRanker {
         private static final String PARAM_SHARED_PREF_NAME = "resolver_ranker_params";
         private static final String BIAS_PREF_KEY = "bias";
-        private static final float LEARNING_RATE = 0.02f;
-        private static final float REGULARIZER_PARAM = 0.1f;
+        private static final String VERSION_PREF_KEY = "version";
+
+        // parameters for a pre-trained model, to initialize the app ranker. When updating the
+        // pre-trained model, please update these params, as well as initModel().
+        private static final int CURRENT_VERSION = 1;
+        private static final float LEARNING_RATE = 0.0001f;
+        private static final float REGULARIZER_PARAM = 0.0001f;
+
         private SharedPreferences mParamSharedPref;
         private ArrayMap<String, Float> mFeatureWeights;
         private float mBias;
 
         public LogisticRegressionAppRanker(Context context) {
             mParamSharedPref = getParamSharedPref(context);
+            initModel();
         }
 
         public float predict(ArrayMap<String, Float> target) {
-            if (target == null || mParamSharedPref == null) {
+            if (target == null) {
                 return 0.0f;
             }
             final int featureSize = target.size();
-            if (featureSize == 0) {
-                return 0.0f;
-            }
             float sum = 0.0f;
-            if (mFeatureWeights == null) {
-                mBias = mParamSharedPref.getFloat(BIAS_PREF_KEY, 0.0f);
-                mFeatureWeights = new ArrayMap<>(featureSize);
-                for (int i = 0; i < featureSize; i++) {
-                    String featureName = target.keyAt(i);
-                    float weight = mParamSharedPref.getFloat(featureName, 0.0f);
-                    sum += weight * target.valueAt(i);
-                    mFeatureWeights.put(featureName, weight);
-                }
-            } else {
-                for (int i = 0; i < featureSize; i++) {
-                    String featureName = target.keyAt(i);
-                    float weight = mFeatureWeights.getOrDefault(featureName, 0.0f);
-                    sum += weight * target.valueAt(i);
-                }
+            for (int i = 0; i < featureSize; i++) {
+                String featureName = target.keyAt(i);
+                float weight = mFeatureWeights.getOrDefault(featureName, 0.0f);
+                sum += weight * target.valueAt(i);
             }
             return (float) (1.0 / (1.0 + Math.exp(-mBias - sum)));
         }
 
         public void update(ArrayMap<String, Float> target, float predict, boolean isSelected) {
-            if (target == null || target.size() == 0) {
+            if (target == null) {
                 return;
             }
             final int featureSize = target.size();
-            if (mFeatureWeights == null) {
-                mBias = 0.0f;
-                mFeatureWeights = new ArrayMap<>(featureSize);
-            }
             float error = isSelected ? 1.0f - predict : -predict;
             for (int i = 0; i < featureSize; i++) {
                 String featureName = target.keyAt(i);
@@ -405,15 +394,13 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
         }
 
         public void commitUpdate() {
-            if (mFeatureWeights == null || mFeatureWeights.size() == 0) {
-                return;
-            }
             SharedPreferences.Editor editor = mParamSharedPref.edit();
             editor.putFloat(BIAS_PREF_KEY, mBias);
             final int size = mFeatureWeights.size();
             for (int i = 0; i < size; i++) {
                 editor.putFloat(mFeatureWeights.keyAt(i), mFeatureWeights.valueAt(i));
             }
+            editor.putInt(VERSION_PREF_KEY, CURRENT_VERSION);
             editor.apply();
         }
 
@@ -431,5 +418,27 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
                     PARAM_SHARED_PREF_NAME + ".xml");
             return context.getSharedPreferences(prefsFile, Context.MODE_PRIVATE);
         }
+
+        private void initModel() {
+            mFeatureWeights = new ArrayMap<>(4);
+            if (mParamSharedPref == null ||
+                    mParamSharedPref.getInt(VERSION_PREF_KEY, 0) < CURRENT_VERSION) {
+                // Initializing the app ranker to a pre-trained model. When updating the pre-trained
+                // model, please increment CURRENT_VERSION, and update LEARNING_RATE and
+                // REGULARIZER_PARAM.
+                mBias = -1.6568f;
+                mFeatureWeights.put(LAUNCH_SCORE, 2.5543f);
+                mFeatureWeights.put(TIME_SPENT_SCORE, 2.8412f);
+                mFeatureWeights.put(RECENCY_SCORE, 0.269f);
+                mFeatureWeights.put(CHOOSER_SCORE, 4.2222f);
+            } else {
+                mBias = mParamSharedPref.getFloat(BIAS_PREF_KEY, 0.0f);
+                mFeatureWeights.put(LAUNCH_SCORE, mParamSharedPref.getFloat(LAUNCH_SCORE, 0.0f));
+                mFeatureWeights.put(
+                        TIME_SPENT_SCORE, mParamSharedPref.getFloat(TIME_SPENT_SCORE, 0.0f));
+                mFeatureWeights.put(RECENCY_SCORE, mParamSharedPref.getFloat(RECENCY_SCORE, 0.0f));
+                mFeatureWeights.put(CHOOSER_SCORE, mParamSharedPref.getFloat(CHOOSER_SCORE, 0.0f));
+            }
+        }
     }
 }