Java tutorial
/* * 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.recommendation; import io.seldon.clustering.recommender.ItemRecommendationAlgorithm; import io.seldon.clustering.recommender.ItemRecommendationResultSet; import io.seldon.clustering.recommender.RecommendationContext; import io.seldon.general.ItemStorage; import java.util.ArrayList; import java.util.Collection; import java.util.Collections; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.commons.lang.StringUtils; import org.apache.log4j.Logger; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Component; /** * @author firemanphil * Date: 22/02/15 * Time: 11:50 */ @Component public class RecentItemsRecommender implements ItemRecommendationAlgorithm { private static final String name = RecentItemsRecommender.class.getSimpleName(); private static Logger logger = Logger.getLogger(RecentItemsRecommender.class.getName()); private final ItemStorage itemStorage; @Autowired public RecentItemsRecommender(ItemStorage itemStorage) { this.itemStorage = itemStorage; } @Override public ItemRecommendationResultSet recommend(String client, Long user, Set<Integer> dimensions, int maxRecsCount, RecommendationContext ctxt, List<Long> recentItemInteractions) { HashMap<Long, Double> recommendations = new HashMap<>(); Set<Long> exclusions; if (ctxt.getMode() == RecommendationContext.MODE.INCLUSION) { logger.warn("Can't run RecentItemsRecommender in inclusion context mode"); return new ItemRecommendationResultSet(name); } else { exclusions = ctxt.getContextItems(); } if (logger.isDebugEnabled()) logger.debug("Running with dimension " + dimensions.toString()); Collection<Long> recList = itemStorage .retrieveRecentlyAddedItems(client, maxRecsCount + exclusions.size(), dimensions).getItems(); if (recList.size() > 0) { double scoreIncr = 1.0 / (double) recList.size(); int count = 0; for (Long item : recList) { if (count >= maxRecsCount) break; else if (!exclusions.contains(item)) recommendations.put(item, 1.0 - (count++ * scoreIncr)); } List<ItemRecommendationResultSet.ItemRecommendationResult> results = new ArrayList<>(); for (Map.Entry<Long, Double> entry : recommendations.entrySet()) { results.add(new ItemRecommendationResultSet.ItemRecommendationResult(entry.getKey(), entry.getValue().floatValue())); } if (logger.isDebugEnabled()) logger.debug("Recent items algorithm returned " + recommendations.size() + " items"); return new ItemRecommendationResultSet(results, name); } else { logger.warn("No items returned for recent items of dimension " + StringUtils.join(dimensions, ",") + " for " + client); } return new ItemRecommendationResultSet(Collections.EMPTY_LIST, name); } @Override public String name() { return name; } }