org.codelibs.elasticsearch.taste.recommender.Recommender Maven / Gradle / Ivy

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/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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.codelibs.elasticsearch.taste.recommender;

import java.util.List;

import org.codelibs.elasticsearch.taste.common.Refreshable;
import org.codelibs.elasticsearch.taste.model.DataModel;

/**
 * 
 * Implementations of this interface can recommend items for a user. Implementations will likely take
 * advantage of several classes in other packages here to compute this.
 * 
 */
public interface Recommender extends Refreshable {

    /**
     * @param userID
     *          user for which recommendations are to be computed
     * @param howMany
     *          desired number of recommendations
     * @return {@link List} of recommended {@link RecommendedItem}s, ordered from most strongly recommend to
     *         least
     */
    List recommend(long userID, int howMany);

    /**
     * @param userID
     *          user for which recommendations are to be computed
     * @param howMany
     *          desired number of recommendations
     * @param rescorer
     *          rescoring function to apply before final list of recommendations is determined
     * @return {@link List} of recommended {@link RecommendedItem}s, ordered from most strongly recommend to
     *         least
     */
    List recommend(long userID, int howMany,
            IDRescorer rescorer);

    /**
     * @param userID
     *          user ID whose preference is to be estimated
     * @param itemID
     *          item ID to estimate preference for
     * @return an estimated preference if the user has not expressed a preference for the item, or else the
     *         user's actual preference for the item. If a preference cannot be estimated, returns
     *         {@link Double#NaN}
     */
    float estimatePreference(long userID, long itemID);

    /**
     * @param userID
     *          user to set preference for
     * @param itemID
     *          item to set preference for
     * @param value
     *          preference value
     */
    void setPreference(long userID, long itemID, float value);

    /**
     * @param userID
     *          user from which to remove preference
     * @param itemID
     *          item for which to remove preference
     */
    void removePreference(long userID, long itemID);

    /**
     * @return underlying {@link DataModel} used by this {@link Recommender} implementation
     */
    DataModel getDataModel();

}