MyMediaLite
3.03
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Base class for rating predictors that use some kind of kNN. More...
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. | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
Protected Attributes | |
UserItemBaseline | baseline_predictor = new UserItemBaseline() |
underlying baseline predictor | |
ICorrelationMatrix | correlation |
Correlation matrix over some kind of entity. | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
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] |
The rating data. | |
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] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates | |
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:
virtual void AddRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Add new ratings and perform incremental training.
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.
virtual bool CanPredict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual, inherited] |
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 BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from Recommender.
abstract float Predict | ( | int | user_id, |
int | item_id | ||
) | [pure virtual, inherited] |
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, TimeAwareBaseline, BPRMF_ItemMapping, MatrixFactorization, BPRLinear, BPRMF_UserMapping, FactorWiseMatrixFactorization, CoClustering, UserItemBaseline, NaiveBayes, SVDPlusPlus, ItemAttributeSVM, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, MF, MostPopularByAttributes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, MostPopularByNominalAttributes, BiPolarSlopeOne, ItemKNN, MostPopular, UserKNN, SlopeOne, ItemKNN, Constant, GlobalAverage, UserKNN, UserAverage, ItemAverage, Random, Random, and Zero.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
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ICollection< int > | ignore_items = null , |
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ICollection< int > | candidate_items = null |
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) | [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.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
virtual void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual, inherited] |
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.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from Recommender.
override string ToString | ( | ) | [inline] |
Return a string representation of the recommender.
The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.
Reimplemented from Recommender.
virtual void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.
UserItemBaseline baseline_predictor = new UserItemBaseline() [protected] |
underlying baseline predictor
ICorrelationMatrix correlation [protected] |
Correlation matrix over some kind of entity.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
float Alpha [get, set] |
Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson.
abstract IBooleanMatrix BinaryDataMatrix [get, protected] |
Return the data matrix that can be used to compute a correlation based on binary data.
If a purely rating-based correlation is used, this property is ignored.
Reimplemented in ItemKNN, UserAttributeKNN, UserKNN, and ItemAttributeKNN.
RatingCorrelationType Correlation [get, set] |
The kind of correlation to use.
abstract EntityType Entity [get, protected] |
uint K [get, set] |
Number of neighbors to take into account for predictions.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual float MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual float MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
uint NumIter [get, set] |
number of iterations used for training the underlying baseline predictor
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, inherited] |
true if items shall be updated when doing incremental updates
Set to false if you do not want any updates to the item model parameters when doing incremental updates.
Implements IIncrementalRecommender.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.
Implements IIncrementalRecommender.
bool WeightedBinary [get, set] |
If set to true, give a lower weight to evidence coming from very frequent entities.