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.baseline; import io.seldon.api.Constants; import io.seldon.api.resource.ConsumerBean; import io.seldon.api.resource.service.ItemService; import io.seldon.clustering.recommender.ItemRecommendationAlgorithm; import io.seldon.clustering.recommender.ItemRecommendationResultSet; import io.seldon.clustering.recommender.RecommendationContext; import io.seldon.recommendation.RecommendationUtils; import io.seldon.recommendation.baseline.MostPopularInSessionFeaturesManager.DimPopularityStore; import io.seldon.recommendation.baseline.MostPopularInSessionFeaturesManager.ItemCount; import java.util.ArrayList; import java.util.Collections; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.commons.collections.CollectionUtils; import org.apache.log4j.Logger; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Component; @Component public class MostPopularInSessionRecommender implements ItemRecommendationAlgorithm { private static Logger logger = Logger.getLogger(MostPopularInSessionRecommender.class.getSimpleName()); private static final String name = MostPopularInSessionRecommender.class.getSimpleName(); private static final String ATTRS_PROPERTY_NAME = "io.seldon.algorithm.popular.attrs"; private static final String DEPTH_PROPERTY_NAME = "io.seldon.algorithm.popular.recent.depth"; MostPopularInSessionFeaturesManager itemsManager; ItemService itemService; @Autowired public MostPopularInSessionRecommender(MostPopularInSessionFeaturesManager itemsManager, ItemService itemService) { this.itemsManager = itemsManager; this.itemService = itemService; } /** * Note this recommender does not respect any dimensions passed in */ @Override public ItemRecommendationResultSet recommend(String client, Long user, Set<Integer> dimensions, int maxRecsCount, RecommendationContext ctxt, List<Long> recentItemInteractions) { RecommendationContext.OptionsHolder options = ctxt.getOptsHolder(); DimPopularityStore store = itemsManager.getClientStore(client, options); if (store == null) { if (logger.isDebugEnabled()) logger.debug("Failed to find popular session data for client " + client); return new ItemRecommendationResultSet( Collections.<ItemRecommendationResultSet.ItemRecommendationResult>emptyList(), name); } String attrs = options.getStringOption(ATTRS_PROPERTY_NAME); int maxDepth = options.getIntegerOption(DEPTH_PROPERTY_NAME); ConsumerBean c = new ConsumerBean(client); String[] attrNames = attrs.split(","); Set<Long> exclusions = Collections.emptySet(); if (ctxt.getMode() == RecommendationContext.MODE.EXCLUSION) { exclusions = ctxt.getContextItems(); } if (logger.isDebugEnabled()) { logger.debug("user " + user + " recentItems:" + recentItemInteractions.toString() + " depth:" + maxDepth + " attrs " + attrs); } Map<Long, Double> scores = new HashMap<>(); for (int depth = 0; depth < maxDepth; depth++) { if (recentItemInteractions.size() <= depth) break; long recentItem = recentItemInteractions.get(depth); Map<String, Integer> attrDims = itemService.getDimensionIdsForItem(c, recentItem); double lowestScore = 1.0; if (logger.isDebugEnabled()) logger.debug("Looking at item " + recentItem + " has attrDim size " + attrDims.size()); for (String attr : attrNames) { Integer dim = attrDims.get(attr); if (dim != null) { List<ItemCount> counts = store.getTopItemsForDimension(dim); if (counts != null) { double maxCount = 0; double lowScore = 1.0; for (ItemCount ic : counts) { if (!exclusions.contains(ic.item)) { Map<String, Integer> attrDimsCandidate = itemService.getDimensionIdsForItem(c, ic.item); if (CollectionUtils.containsAny(dimensions, attrDimsCandidate.values()) || dimensions.contains(Constants.DEFAULT_DIMENSION)) { if (logger.isDebugEnabled()) logger.debug("Adding item " + ic.item + " from dimension " + attr); if (maxCount == 0) maxCount = ic.count; double normCount = (ic.count / maxCount) * lowestScore; //scale to be a score lower than previous values if any if (scores.containsKey(ic.item)) scores.put(ic.item, scores.get(ic.item) + normCount); else scores.put(ic.item, normCount); lowScore = normCount; if (scores.size() >= maxRecsCount) break; } else { if (logger.isDebugEnabled()) logger.debug("Ignoring prospective item " + ic.item + " as not in dimensions " + dimensions.toString()); } } else { if (logger.isDebugEnabled()) logger.debug("Excluding item " + ic.item); } } lowestScore = lowScore;//update lowest from this loop } else { if (logger.isDebugEnabled()) logger.debug("No counts for dimension " + dim + " attribute name " + attr); } } else { logger.warn("Failed to find attr " + attr + " for item " + recentItem); } if (scores.size() >= maxRecsCount) break; } } Map<Long, Double> scaledScores = RecommendationUtils.rescaleScoresToOne(scores, maxRecsCount); List<ItemRecommendationResultSet.ItemRecommendationResult> results = new ArrayList<>(); for (Map.Entry<Long, Double> e : scaledScores.entrySet()) { results.add(new ItemRecommendationResultSet.ItemRecommendationResult(e.getKey(), e.getValue().floatValue())); } if (logger.isDebugEnabled()) logger.debug("Returning " + results.size() + " recommendations"); return new ItemRecommendationResultSet(results, name); } @Override public String name() { return name; } }