/*
* Seldon -- open source prediction engine
* =======================================
*
* Copyright 2011-2015 Seldon Technologies Ltd and Rummble Ltd (http://www.seldon.io/)
*
* ********************************************************************************************
*
* 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 io.seldon.clustering.recommender;
import io.seldon.clustering.recommender.jdo.JdoCountRecommenderUtils;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.apache.log4j.Logger;
import org.springframework.beans.factory.annotation.Autowired;
/**
* @author firemanphil
* Date: 11/12/14
* Time: 15:20
*/
public abstract class BaseItemClusterCountsRecommender {
private static final String MIN_ITEMS_FOR_VALID_CLUSTER_OPTION_NAME
= "io.seldon.algorithm.clusters.minnumberitemsforvalidclusterresult";
private static final String DECAY_RATE_OPTION_NAME = "io.seldon.algorithm.clusters.decayratesecs";
private static final String CLUSTER_ALG_OPTION_NAME = "io.seldon.algorithm.clusters.itemalg";
private static Logger logger = Logger.getLogger( BaseItemClusterCountsRecommender.class.getName() );
@Autowired
JdoCountRecommenderUtils cUtils;
ItemRecommendationResultSet recommend(String recommenderName, String recommenderType, String client, Long user, Set<Integer> dimensions, int maxRecsCount,
RecommendationContext ctxt) {
RecommendationContext.OptionsHolder optionsHolder = ctxt.getOptsHolder();
if (ctxt.getCurrentItem() != null) {
Set<Long> exclusions = Collections.emptySet();
if (ctxt.getMode() == RecommendationContext.MODE.EXCLUSION) {
exclusions = ctxt.getContextItems();
}
CountRecommender r = cUtils.getCountRecommender(client);
if (r != null) {
long t1 = System.currentTimeMillis();
Integer minClusterItems = optionsHolder.getIntegerOption(MIN_ITEMS_FOR_VALID_CLUSTER_OPTION_NAME);
Double decayRate = optionsHolder.getDoubleOption(DECAY_RATE_OPTION_NAME);
String clusterAlgorithm = optionsHolder.getStringOption(CLUSTER_ALG_OPTION_NAME);
Map<Long, Double> recommendations = r.recommendUsingItem(recommenderType,ctxt.getCurrentItem(), dimensions,
maxRecsCount, exclusions, decayRate, clusterAlgorithm, minClusterItems);
long t2 = System.currentTimeMillis();
logger.debug("Recommendation via cluster counts for item " + ctxt.getCurrentItem() + " for user " + user + " took " + (t2 - t1));
List<ItemRecommendationResultSet.ItemRecommendationResult> results = new ArrayList<>();
for (Map.Entry<Long, Double> entry : recommendations.entrySet()) {
results.add(new ItemRecommendationResultSet.ItemRecommendationResult(entry.getKey(), entry.getValue().floatValue()));
}
return new ItemRecommendationResultSet(results, recommenderName);
} else {
return new ItemRecommendationResultSet(Collections.<ItemRecommendationResultSet.ItemRecommendationResult>emptyList(), recommenderName);
}
} else {
logger.info("Can't cluster count for item for user " + user + " as no current item passed in");
return new ItemRecommendationResultSet(Collections.<ItemRecommendationResultSet.ItemRecommendationResult>emptyList(), recommenderName);
}
}
}