org.codelibs.elasticsearch.taste.eval.RecommenderIRStatsEvaluator Maven / Gradle / Ivy
/**
* 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.eval;
import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.recommender.IDRescorer;
/**
*
* Implementations collect information retrieval-related statistics on a
* {@link org.codelibs.elasticsearch.taste.recommender.Recommender}'s performance, including precision, recall and
* f-measure.
*
*
*
* See Information retrieval.
*/
public interface RecommenderIRStatsEvaluator {
/**
* @param recommenderBuilder
* object that can build a {@link org.codelibs.elasticsearch.taste.recommender.Recommender} to test
* @param dataModelBuilder
* {@link DataModelBuilder} to use, or if null, a default {@link DataModel} implementation will be
* used
* @param dataModel
* dataset to test on
* @param rescorer
* if any, to use when computing recommendations
* @param at
* as in, "precision at 5". The number of recommendations to consider when evaluating precision,
* etc.
* @param relevanceThreshold
* items whose preference value is at least this value are considered "relevant" for the purposes
* of computations
* @return {@link IRStatistics} with resulting precision, recall, etc.
*/
IRStatistics evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder, DataModel dataModel,
IDRescorer rescorer, int at, double relevanceThreshold,
double evaluationPercentage);
}