Public Member Functions |
virtual void | AddRatings (IRatings new_ratings) |
| Add new ratings and perform incremental training
|
virtual bool | CanPredict (int user_id, int item_id) |
| Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination
|
Object | Clone () |
| create a shallow copy of the object
|
override void | LoadModel (string filename) |
| Get the model parameters from a file
|
abstract float | Predict (int user_id, int item_id) |
| Predict rating or score for a given user-item combination
|
IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
| Recommend items for a given user
|
virtual
System.Collections.Generic.IList
< Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
virtual void | RemoveItem (int item_id) |
| Remove all feedback by one item
|
virtual void | RemoveRatings (IDataSet ratings_to_delete) |
| Remove existing ratings and perform "incremental" training
|
virtual void | RemoveUser (int user_id) |
| Remove all feedback by one user
|
override void | SaveModel (string filename) |
| Save the model parameters to a file
|
override string | ToString () |
| Return a string representation of the recommender
|
override void | Train () |
| Learn the model parameters of the recommender from the training data
|
virtual void | UpdateRatings (IRatings new_ratings) |
| Update existing ratings and perform incremental training
|
Properties |
float | Alpha [get, set] |
| Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson
|
abstract IBooleanMatrix | BinaryDataMatrix [get] |
| Return the data matrix that can be used to compute a correlation based on binary data
|
RatingCorrelationType | Correlation [get, set] |
| The kind of correlation to use
|
abstract EntityType | Entity [get] |
| The entity type of the neighbors used for rating prediction
|
uint | K [get, set] |
| Number of neighbors to take into account for predictions
|
int | MaxItemID [get, set] |
| Maximum item ID
|
virtual float | MaxRating [get, set] |
| Maximum rating value
|
int | MaxUserID [get, set] |
| Maximum user ID
|
virtual float | MinRating [get, set] |
| Minimum rating value
|
uint | NumIter [get, set] |
| number of iterations used for training the underlying baseline predictor
|
override IRatings | Ratings [set] |
float | RegI [get, set] |
| regularization constant for the item bias of the underlying baseline predictor
|
float | RegU [get, set] |
| regularization constant for the user bias of the underlying baseline predictor
|
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
bool | WeightedBinary [get, set] |
| If set to true, give a lower weight to evidence coming from very frequent entities
|
Base class for rating predictors that use some kind of kNN
The method is described in section 2.2 of the paper below. One difference is that we support several iterations of alternating optimization, instead of just one.
Literature:
- See Also
- MyMediaLite.ItemRecommendation.KNN
virtual bool CanPredict |
( |
int |
user_id, |
|
|
int |
item_id |
|
) |
| |
|
inlinevirtualinherited |
Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination
It is up to the recommender implementor to decide when a prediction is useful, and to document it accordingly.
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- true if a useful prediction can be made, false otherwise
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
abstract float Predict |
( |
int |
user_id, |
|
|
int |
item_id |
|
) |
| |
|
pure virtualinherited |
Predict rating or score for a given user-item combination
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- the predicted score/rating for the given user-item combination
Implements IRecommender.
Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, BPRLinear, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.