public GenericItemBasedRecommender(IDataModel dataModel, IItemSimilarity similarity) :
this(dataModel,
similarity,
AbstractRecommender.GetDefaultCandidateItemsStrategy(),
getDefaultMostSimilarItemsCandidateItemsStrategy())
{
}
internal DataModelSimilaritiesIterator(IItemSimilarity otherSimilarity, long[] itemIDs)
{
this.otherSimilarity = otherSimilarity;
this.itemIDs = itemIDs;
i = 0;
itemID1 = itemIDs[0];
j = 1;
}
internal MultiMostSimilarEstimator(long[] toItemIDs, IItemSimilarity similarity, IRescorer <Tuple <long, long> > rescorer,
bool excludeItemIfNotSimilarToAll)
{
this.toItemIDs = toItemIDs;
this.similarity = similarity;
this.rescorer = rescorer;
this.excludeItemIfNotSimilarToAll = excludeItemIfNotSimilarToAll;
}
/// Creates this on top of the given {@link ItemSimilarity}.
/// The cache size is capped by the given size.
public CachingItemSimilarity(IItemSimilarity similarity, int maxCacheSize)
{
//Preconditions.checkArgument(similarity != null, "similarity is null");
this.similarity = similarity;
this.similarityCache = new Cache <Tuple <long, long>, Double>(new SimilarityRetriever(similarity), maxCacheSize);
this.refreshHelper = new RefreshHelper(() => {
similarityCache.Clear();
});
refreshHelper.AddDependency(similarity);
}
/// <p>
/// Like {@link #GenericItemSimilarity(ItemSimilarity, DataModel)} )}, but will only keep the specified
/// number of similarities from the given {@link DataModel}. It will keep those with the highest similarity
/// -- those that are therefore most important.
/// </p>
///
/// <p>
/// Thanks to tsmorton for suggesting this and providing part of the implementation.
/// </p>
///
/// @param otherSimilarity
/// other {@link ItemSimilarity} to get similarities from
/// @param dataModel
/// data model to get items from
/// @param maxToKeep
/// maximum number of similarities to keep
/// @throws TasteException
/// if an error occurs while accessing the {@link DataModel} items
public GenericItemSimilarity(IItemSimilarity otherSimilarity,
IDataModel dataModel,
int maxToKeep)
{
long[] itemIDs = GenericUserSimilarity.longIteratorToList(dataModel.GetItemIDs());
var it = new DataModelSimilaritiesIterator(otherSimilarity, itemIDs);
var keptSimilarities = TopItems.GetTopItemItemSimilarities(maxToKeep, it);
initSimilarityMaps(keptSimilarities.GetEnumerator());
}
public GenericItemBasedRecommender(IDataModel dataModel,
IItemSimilarity similarity,
ICandidateItemsStrategy candidateItemsStrategy,
IMostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy) :
base(dataModel, candidateItemsStrategy)
{
//Preconditions.checkArgument(similarity != null, "similarity is null");
this.similarity = similarity;
//Preconditions.checkArgument(mostSimilarItemsCandidateItemsStrategy != null,
// "mostSimilarItemsCandidateItemsStrategy is null");
this.mostSimilarItemsCandidateItemsStrategy = mostSimilarItemsCandidateItemsStrategy;
this.refreshHelper = new RefreshHelper(() => {
capper = buildCapper();
});
refreshHelper.AddDependency(dataModel);
refreshHelper.AddDependency(similarity);
refreshHelper.AddDependency(candidateItemsStrategy);
refreshHelper.AddDependency(mostSimilarItemsCandidateItemsStrategy);
capper = buildCapper();
}
public AllSimilarItemsCandidateItemsStrategy(IItemSimilarity similarity) {
//Preconditions.checkArgument(similarity != null, "similarity is null");
this.similarity = similarity;
}
public GenericBooleanPrefItemBasedRecommender(IDataModel dataModel, IItemSimilarity similarity,
ICandidateItemsStrategy candidateItemsStrategy, IMostSimilarItemsCandidateItemsStrategy
mostSimilarItemsCandidateItemsStrategy)
: base(dataModel, similarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy) {
}
public GenericBooleanPrefItemBasedRecommender(IDataModel dataModel, IItemSimilarity similarity)
: base(dataModel, similarity) {
}
public AllSimilarItemsCandidateItemsStrategy(IItemSimilarity similarity)
{
//Preconditions.checkArgument(similarity != null, "similarity is null");
this.similarity = similarity;
}
public GenericBooleanPrefItemBasedRecommender(IDataModel dataModel, IItemSimilarity similarity,
ICandidateItemsStrategy candidateItemsStrategy, IMostSimilarItemsCandidateItemsStrategy
mostSimilarItemsCandidateItemsStrategy)
: base(dataModel, similarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy)
{
}
public GenericBooleanPrefItemBasedRecommender(IDataModel dataModel, IItemSimilarity similarity)
: base(dataModel, similarity)
{
}
internal SimilarityRetriever(IItemSimilarity similarity)
{
this.similarity = similarity;
}
/// Creates this on top of the given {@link ItemSimilarity}.
/// The cache is sized according to properties of the given {@link DataModel}.
public CachingItemSimilarity(IItemSimilarity similarity, IDataModel dataModel) : this(similarity, dataModel.GetNumItems())
{
;
}
/// <p>
/// Builds a list of item-item similarities given an {@link ItemSimilarity} implementation and a
/// {@link DataModel}, rather than a list of {@link ItemItemSimilarity}s.
/// </p>
///
/// <p>
/// It's valid to build a {@link GenericItemSimilarity} this way, but perhaps missing some of the point of an
/// item-based recommender. Item-based recommenders use the assumption that item-item similarities are
/// relatively fixed, and might be known already independent of user preferences. Hence it is useful to
/// inject that information, using {@link #GenericItemSimilarity(Iterable)}.
/// </p>
///
/// @param otherSimilarity
/// other {@link ItemSimilarity} to get similarities from
/// @param dataModel
/// data model to get items from
/// @throws TasteException
/// if an error occurs while accessing the {@link DataModel} items
public GenericItemSimilarity(IItemSimilarity otherSimilarity, IDataModel dataModel)
{
long[] itemIDs = GenericUserSimilarity.longIteratorToList(dataModel.GetItemIDs());
initSimilarityMaps(new DataModelSimilaritiesIterator(otherSimilarity, itemIDs));
}
public MostSimilarEstimator(long toItemID, IItemSimilarity similarity, IRescorer <Tuple <long, long> > rescorer)
{
this.toItemID = toItemID;
this.similarity = similarity;
this.rescorer = rescorer;
}
