org.codelibs.elasticsearch.taste.eval.RelevantItemsDataSplitter 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,
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 * See the License for the specific language governing permissions and
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package org.codelibs.elasticsearch.taste.eval;

import org.codelibs.elasticsearch.taste.common.FastByIDMap;
import org.codelibs.elasticsearch.taste.common.FastIDSet;
import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.model.PreferenceArray;

/**
 * Implementations of this interface determine the items that are considered relevant,
 * and splits data into a training and test subset, for purposes of precision/recall
 * tests as implemented by implementations of {@link RecommenderIRStatsEvaluator}.
 */
public interface RelevantItemsDataSplitter {

    /**
     * During testing, relevant items are removed from a particular users' preferences,
     * and a model is build using this user's other preferences and all other users.
     *
     * @param at                 Maximum number of items to be removed
     * @param relevanceThreshold Minimum strength of preference for an item to be considered
     *                           relevant
     * @return IDs of relevant items
     */
    FastIDSet getRelevantItemsIDs(long userID, int at,
            double relevanceThreshold, DataModel dataModel);

    /**
     * Adds a single user and all their preferences to the training model.
     *
     * @param userID          ID of user whose preferences we are trying to predict
     * @param relevantItemIDs IDs of items considered relevant to that user
     * @param trainingUsers   the database of training preferences to which we will
     *                        append the ones for otherUserID.
     * @param otherUserID     for whom we are adding preferences to the training model
     */
    void processOtherUser(long userID, FastIDSet relevantItemIDs,
            FastByIDMap trainingUsers, long otherUserID,
            DataModel dataModel);

}