org.conqat.lib.commons.datamining.AssociationRuleRecommender Maven / Gradle / Ivy

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Provides common utility functions

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/*
 * Copyright (c) CQSE GmbH
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.conqat.lib.commons.datamining;

import java.io.Serializable;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

/**
 * Based on a set of association rules, this recommender can recommend items for a given basket.
 */
public class AssociationRuleRecommender implements IRecommender, Serializable {

	/** Serial ID */
	private static final long serialVersionUID = 1L;

	/** The database containing the ratings. */
	private final RecommenderRatingDatabase ratingDatabase;

	/** The mined association rules */
	private final Set> associationRules;

	/** Constructor. */
	public AssociationRuleRecommender(RecommenderRatingDatabase ratingDatabase, float supportThreshold,
			float confidenceThreshold) {
		this.ratingDatabase = ratingDatabase;
		Set> baskets = new HashSet<>();
		for (IRecommenderUser user : ratingDatabase.getUsers()) {
			baskets.add(ratingDatabase.getLikedItems(user));
		}
		associationRules = new AssociationRuleMiner(supportThreshold, confidenceThreshold)
				.mineAssociationRules(baskets);
	}

	/**
	 * Recommends items for the given user. The returned set may be empty, if no recommendations could
	 * be made.
	 */
	@Override
	public Set> recommend(IRecommenderUser user) {
		Map> itemToRecommendationMap = new HashMap<>();
		Set items = ratingDatabase.getLikedItems(user);

		for (AssociationRule rule : associationRules) {
			if (items.containsAll(rule.getItemSet()) && !items.contains(rule.getAssociatedItem())) {
				T item = rule.getAssociatedItem();
				double confidence = rule.getConfidence();

				Recommendation existingRecommendation = itemToRecommendationMap.get(item);

				if (existingRecommendation == null || confidence > existingRecommendation.getConfidence()) {
					itemToRecommendationMap.put(item, new Recommendation(item, confidence));
				}
			}
		}
		return new HashSet<>(itemToRecommendationMap.values());
	}
}