/*
* 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;
}
}