Skip to content

Instantly share code, notes, and snippets.

@gedankennebel
Created May 3, 2014 23:44
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save gedankennebel/36cde4484bfb851a1691 to your computer and use it in GitHub Desktop.
Save gedankennebel/36cde4484bfb851a1691 to your computer and use it in GitHub Desktop.
Act like a Mahout CachingRecommender + contains ItemBased-Features like recommenedBecause or mostSimilarItem
import com.google.common.base.Preconditions;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.Cache;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.Retriever;
import org.apache.mahout.cf.taste.impl.model.PlusAnonymousUserDataModel;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Rescorer;
import org.apache.mahout.common.LongPair;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
public class CachingItemRecommender implements ItemBasedRecommender {
private static final Logger log = LoggerFactory.getLogger(CachingItemRecommender.class);
private final ItemBasedRecommender recommender;
private final int[] maxHowMany;
private final Retriever<Long, Recommendations> recommendationsRetriever;
private final Cache<Long, Recommendations> recommendationCache;
private final Cache<LongPair, Float> estimatedPrefCache;
private final RefreshHelper refreshHelper;
private IDRescorer currentRescorer;
public CachingItemRecommender(ItemBasedRecommender recommender) throws TasteException {
Preconditions.checkArgument(recommender != null, "recommender is null");
this.recommender = recommender;
maxHowMany = new int[]{1};
// Use "num users" as an upper limit on cache size. Rough guess.
int numUsers = recommender.getDataModel().getNumUsers();
recommendationsRetriever = new RecommendationRetriever();
recommendationCache = new Cache<>(recommendationsRetriever, numUsers);
estimatedPrefCache = new Cache<>(new EstimatedPrefRetriever(), numUsers);
refreshHelper = new RefreshHelper(() -> {
clear();
return null;
});
refreshHelper.addDependency(recommender);
}
private void setCurrentRescorer(IDRescorer rescorer) {
if (rescorer == null) {
if (currentRescorer != null) {
currentRescorer = null;
clear();
}
} else {
if (!rescorer.equals(currentRescorer)) {
currentRescorer = rescorer;
clear();
}
}
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany) throws TasteException {
return recommend(userID, howMany, null);
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
synchronized (maxHowMany) {
if (howMany > maxHowMany[0]) {
maxHowMany[0] = howMany;
}
}
// Special case, avoid caching an anonymous user
if (userID == PlusAnonymousUserDataModel.TEMP_USER_ID) {
return recommendationsRetriever.get(PlusAnonymousUserDataModel.TEMP_USER_ID).getItems();
}
setCurrentRescorer(rescorer);
Recommendations recommendations = recommendationCache.get(userID);
if (recommendations.getItems().size() < howMany && !recommendations.isNoMoreRecommendableItems()) {
clear(userID);
recommendations = recommendationCache.get(userID);
if (recommendations.getItems().size() < howMany) {
recommendations.setNoMoreRecommendableItems(true);
}
}
List<RecommendedItem> recommendedItems = recommendations.getItems();
return recommendedItems.size() > howMany ? recommendedItems.subList(0, howMany) : recommendedItems;
}
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
return estimatedPrefCache.get(new LongPair(userID, itemID));
}
@Override
public void setPreference(long userID, long itemID, float value) throws TasteException {
recommender.setPreference(userID, itemID, value);
clear(userID);
}
@Override
public void removePreference(long userID, long itemID) throws TasteException {
recommender.removePreference(userID, itemID);
clear(userID);
}
@Override
public DataModel getDataModel() {
return recommender.getDataModel();
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
/**
* <p>
* Clears cached recommendations for the given user.
* </p>
*
* @param userID clear cached data associated with this user ID
*/
public void clear(final long userID) {
log.debug("Clearing recommendations for user ID '{}'", userID);
recommendationCache.remove(userID);
estimatedPrefCache.removeKeysMatching(userItemPair -> userItemPair.getFirst() == userID);
}
/**
* <p>
* Clears all cached recommendations.
* </p>
*/
public void clear() {
log.debug("Clearing all recommendations...");
recommendationCache.clear();
estimatedPrefCache.clear();
}
@Override
public String toString() {
return "CachingRecommender[recommender:" + recommender + ']';
}
@Override
public List<RecommendedItem> mostSimilarItems(long itemID, int howMany) throws TasteException {
return mostSimilarItems(itemID, howMany);
}
@Override
public List<RecommendedItem> mostSimilarItems(long itemID, int howMany, Rescorer<LongPair> rescorer) throws TasteException {
return recommender.mostSimilarItems(itemID, howMany, rescorer);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany) throws TasteException {
return recommender.mostSimilarItems(itemIDs, howMany);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer) throws TasteException {
return recommender.mostSimilarItems(itemIDs, howMany, rescorer);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany, boolean excludeItemIfNotSimilarToAll) throws TasteException {
return recommender.mostSimilarItems(itemIDs, howMany, excludeItemIfNotSimilarToAll);
}
@Override
public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer, boolean excludeItemIfNotSimilarToAll) throws TasteException {
return recommender.mostSimilarItems(itemIDs, howMany, rescorer, excludeItemIfNotSimilarToAll);
}
@Override
public List<RecommendedItem> recommendedBecause(long userID, long itemID, int howMany) throws TasteException {
return recommender.recommendedBecause(userID, itemID, howMany);
}
private final class EstimatedPrefRetriever implements Retriever<LongPair, Float> {
@Override
public Float get(LongPair key) throws TasteException {
long userID = key.getFirst();
long itemID = key.getSecond();
log.debug("Retrieving estimated preference for user ID '{}' and item ID '{}'", userID, itemID);
return recommender.estimatePreference(userID, itemID);
}
}
private final class RecommendationRetriever implements Retriever<Long, Recommendations> {
@Override
public Recommendations get(Long key) throws TasteException {
log.debug("Retrieving new recommendations for user ID '{}'", key);
int howMany = maxHowMany[0];
IDRescorer rescorer = currentRescorer;
List<RecommendedItem> recommendations =
rescorer == null ? recommender.recommend(key, howMany) : recommender.recommend(key, howMany, rescorer);
return new Recommendations(Collections.unmodifiableList(recommendations));
}
}
private static final class Recommendations {
private final List<RecommendedItem> items;
private boolean noMoreRecommendableItems;
private Recommendations(List<RecommendedItem> items) {
this.items = items;
}
List<RecommendedItem> getItems() {
return items;
}
boolean isNoMoreRecommendableItems() {
return noMoreRecommendableItems;
}
void setNoMoreRecommendableItems(boolean noMoreRecommendableItems) {
this.noMoreRecommendableItems = noMoreRecommendableItems;
}
}
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment