MyMediaLite
3.11
|
Base class for rating predictors that support incremental training More...
Public Member Functions | |
virtual void | AddRatings (IRatings new_ratings) |
Add new ratings and perform incremental training More... | |
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 More... | |
Object | Clone () |
create a shallow copy of the object More... | |
IncrementalRatingPredictor () | |
Default constructor More... | |
virtual void | LoadModel (string file) |
Get the model parameters from a file More... | |
abstract float | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination More... | |
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 More... | |
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 More... | |
virtual void | RemoveRatings (IDataSet ratings_to_delete) |
Remove existing ratings and perform "incremental" training More... | |
virtual void | RemoveUser (int user_id) |
Remove all feedback by one user More... | |
virtual void | SaveModel (string file) |
Save the model parameters to a file More... | |
override string | ToString () |
Return a string representation of the recommender More... | |
abstract void | Train () |
Learn the model parameters of the recommender from the training data More... | |
virtual void | UpdateRatings (IRatings new_ratings) |
Update existing ratings and perform incremental training More... | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
Protected Attributes | |
float | max_rating |
Maximum rating value More... | |
float | min_rating |
Minimum rating value More... | |
IRatings | ratings |
rating data More... | |
Properties | |
int | MaxItemID [get, set] |
Maximum item ID More... | |
virtual float | MaxRating [get, set] |
Maximum rating value More... | |
int | MaxUserID [get, set] |
Maximum user ID More... | |
virtual float | MinRating [get, set] |
Minimum rating value More... | |
virtual IRatings | Ratings [get, set] |
The rating data More... | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
Base class for rating predictors that support incremental training
|
inline |
Default constructor
|
inlinevirtual |
Add new ratings and perform incremental training
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.
|
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.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
|
inlineinherited |
create a shallow copy of the object
|
inlinevirtualinherited |
Get the model parameters from a file
filename | the name of the file to read from |
Implements IRecommender.
Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, EntityAverage, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.
|
pure virtualinherited |
Predict rating or score for a given user-item combination
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, 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.
|
inherited |
Recommend items for a given user
user_id | the user ID |
n | the number of items to recommend, -1 for as many as possible |
ignore_items | collection if items that should not be returned; if null, use empty collection |
candidate_items | the candidate items to choose from; if null, use all items |
Implemented in WeightedEnsemble, and Ensemble.
|
inlinevirtual |
Remove all feedback by one item
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
|
inlinevirtual |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, ItemAverage, and GlobalAverage.
|
inlinevirtual |
Remove all feedback by one user
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
|
inlinevirtualinherited |
Save the model parameters to a file
filename | the name of the file to write to |
Implements IRecommender.
Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, MostPopular, KNN, SLIM, MostPopularByAttributes, EntityAverage, ExternalItemRecommender, ExternalRatingPredictor, GlobalAverage, Constant, Random, Random, and Zero.
|
inlineinherited |
Return a string representation of the recommender
The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.
Implements IRecommender.
|
pure virtualinherited |
Learn the model parameters of the recommender from the training data
Implements IRecommender.
Implemented in BiasedMatrixFactorization, TimeAwareBaseline, BPRMF, KNN, MatrixFactorization, KNN, LatentFeatureLogLinearModel, CoClustering, BiPolarSlopeOne, FactorWiseMatrixFactorization, BPRSLIM, SlopeOne, UserItemBaseline, LeastSquareSLIM, TimeAwareBaselineWithFrequencies, SVDPlusPlus, GSVDPlusPlus, SLIM, NaiveBayes, SigmoidCombinedAsymmetricFactorModel, MF, MostPopularByAttributes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SigmoidSVDPlusPlus, MostPopular, ExternalItemRecommender, ExternalRatingPredictor, MultiCoreBPRMF, WeightedBPRMF, Constant, ItemKNN, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.
|
inlinevirtual |
Update existing ratings and perform incremental training
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.
|
protectedinherited |
Maximum rating value
|
protectedinherited |
Minimum rating value
|
protectedinherited |
rating data
|
getsetinherited |
Maximum item ID
|
getsetinherited |
Maximum rating value
|
getsetinherited |
Maximum user ID
|
getsetinherited |
Minimum rating value