public RecommendedBecauseEstimator(User user,
Item recommendedItem,
ItemCorrelation correlation)
{
this.user = user;
this.recommendedItem = recommendedItem;
this.correlation = correlation;
}
public MultiMostSimilarEstimator(List <Item> toItems,
ItemCorrelation correlation,
Rescorer <Pair <Item, Item> > rescorer)
{
this.toItems = toItems;
this.correlation = correlation;
this.rescorer = rescorer;
}
internal MostSimilarEstimator(Item toItem,
ItemCorrelation correlation,
Rescorer <Pair <Item, Item> > rescorer)
{
this.toItem = toItem;
this.correlation = correlation;
this.rescorer = rescorer;
}
public GenericItemBasedRecommender(DataModel dataModel, ItemCorrelation correlation)
: base(dataModel)
{
if (correlation == null)
{
throw new ArgumentNullException("Correlation is null");
}
this.correlation = correlation;
this.refreshLock = new ReentrantLock();
}
/// <summary>
/// <p>Builds a list of item-item correlations given an {@link ItemCorrelation} implementation and a
/// <see cref="DataModel">DataModel</see>, rather than a list of {@link ItemItemCorrelation}s.</p>
/// <p>It's valid to build a <see cref="GenericItemCorrelation"/> this way, but perhaps missing some of the point
/// of an item-based Recommender. Item-based recommenders use the assumption that item-item correlations
/// are relatively fixed, and might be known already independent of user preferences. Hence it is useful
/// to inject that information, using {@link GenericItemCorrelation(java.util.Collection)}.</p>
/// </summary>
/// <param name="otherCorrelation">otherCorrelation other {@link ItemCorrelation} to get correlations from</param>
/// <param name="dataModel">dataModel data Model to get {@link Item}s from</param>
public GenericItemCorrelation(ItemCorrelation otherCorrelation, DataModel dataModel)
{
List <Item> items = EnumeratorUtils.EnumerableToList <Item>(dataModel.GetItems());
int size = items.Count;
for (int i = 0; i < size; i++)
{
Item item1 = items[i];
for (int j = i + 1; j < size; j++)
{
Item item2 = items[j];
double correlation = otherCorrelation.GetItemCorrelation(item1, item2);
Dictionary <Item, Double> map = null;
if (!correlationMaps.TryGetValue(item1, out map))
{
map = new Dictionary <Item, Double>(1009);
correlationMaps.Add(item1, map);
}
map.Add(item2, correlation);
}
}
}
/// <summary>
/// <p>Builds a list of item-item correlations given an {@link ItemCorrelation} implementation and a
/// <see cref="DataModel">DataModel</see>, rather than a list of {@link ItemItemCorrelation}s.</p>
/// <p>It's valid to build a <see cref="GenericItemCorrelation"/> this way, but perhaps missing some of the point
/// of an item-based Recommender. Item-based recommenders use the assumption that item-item correlations
/// are relatively fixed, and might be known already independent of user preferences. Hence it is useful
/// to inject that information, using {@link GenericItemCorrelation(java.util.Collection)}.</p>
/// </summary>
/// <param name="otherCorrelation">otherCorrelation other {@link ItemCorrelation} to get correlations from</param>
/// <param name="dataModel">dataModel data Model to get {@link Item}s from</param>
public GenericItemCorrelation(ItemCorrelation otherCorrelation, DataModel dataModel)
{
List<Item> items = EnumeratorUtils.EnumerableToList<Item>(dataModel.GetItems());
int size = items.Count;
for (int i = 0; i < size; i++)
{
Item item1 = items[i];
for (int j = i + 1; j < size; j++)
{
Item item2 = items[j];
double correlation = otherCorrelation.GetItemCorrelation(item1, item2);
Dictionary<Item, Double> map = null;
if (!correlationMaps.TryGetValue(item1, out map))
{
map = new Dictionary<Item, Double>(1009);
correlationMaps.Add(item1, map);
}
map.Add(item2, correlation);
}
}
}
