a861d11027
- This is not a functional change and it only updates the model weights which affects the ranking of suggestion items. The previous weights were tuned to force the ranking to have immediate response to signal changes. But, the current weights are tuned to maximize the user interactions with suggestion. Test: RunSettingsRoboTests & also manually testing suggestions Bug: 64093782 Change-Id: I9f50ed6c4ed22d1a14110ec61156c14ba74aef64
84 lines
3.3 KiB
Java
84 lines
3.3 KiB
Java
/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License
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*/
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package com.android.settings.dashboard.suggestions;
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import com.android.settingslib.drawer.Tile;
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import android.support.annotation.VisibleForTesting;
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import java.util.Collections;
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import java.util.Comparator;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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public class SuggestionRanker {
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private static final String TAG = "SuggestionRanker";
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// The following coefficients form a linear model, which mixes the features to obtain a
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// relevance metric for ranking the suggestion items. This model is learned with off-line data
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// by training a binary classifier to detect the clicked items. The higher the obtained
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// relevance metric, the higher chance of getting clicked.
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private static final Map<String, Double> WEIGHTS = new HashMap<String, Double>() {{
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put(SuggestionFeaturizer.FEATURE_IS_SHOWN, 5.05140842519);
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put(SuggestionFeaturizer.FEATURE_IS_DISMISSED, 2.29641455171);
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put(SuggestionFeaturizer.FEATURE_IS_CLICKED, -2.98812233623);
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put(SuggestionFeaturizer.FEATURE_TIME_FROM_LAST_SHOWN, 5.02807250202);
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put(SuggestionFeaturizer.FEATURE_TIME_FROM_LAST_DISMISSED, 2.49589700842);
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put(SuggestionFeaturizer.FEATURE_TIME_FROM_LAST_CLICKED, -4.3377039948);
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put(SuggestionFeaturizer.FEATURE_SHOWN_COUNT, -2.35993512546);
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}};
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private final SuggestionFeaturizer mSuggestionFeaturizer;
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private final Map<Tile, Double> relevanceMetrics;
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Comparator<Tile> suggestionComparator = new Comparator<Tile>() {
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@Override
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public int compare(Tile suggestion1, Tile suggestion2) {
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return relevanceMetrics.get(suggestion1) < relevanceMetrics.get(suggestion2) ? 1 : -1;
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}
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};
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public SuggestionRanker(SuggestionFeaturizer suggestionFeaturizer) {
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mSuggestionFeaturizer = suggestionFeaturizer;
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relevanceMetrics = new HashMap<Tile, Double>();
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}
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public void rankSuggestions(final List<Tile> suggestions, List<String> suggestionIds) {
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relevanceMetrics.clear();
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Map<String, Map<String, Double>> features = mSuggestionFeaturizer.featurize(suggestionIds);
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for (int i = 0; i < suggestionIds.size(); i++) {
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relevanceMetrics.put(suggestions.get(i),
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getRelevanceMetric(features.get(suggestionIds.get(i))));
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}
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Collections.sort(suggestions, suggestionComparator);
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}
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@VisibleForTesting
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double getRelevanceMetric(Map<String, Double> features) {
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double sum = 0;
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if (features == null) {
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return sum;
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}
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for (String feature : WEIGHTS.keySet()) {
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sum += WEIGHTS.get(feature) * features.get(feature);
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}
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return sum;
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}
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}
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