Interface for rating predictors which support incremental training More...

Inheritance diagram for IIncrementalRatingPredictor:

Public Member Functions
void	AddRatings (IRatings ratings)
	Add new ratings and perform incremental training More...

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...

void	LoadModel (string filename)
	Get the model parameters from a file More...

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...

void	RemoveItem (int item_id)
	Remove all feedback by one item More...

void	RemoveRatings (IDataSet ratings)
	Remove existing ratings and perform "incremental" training More...

void	RemoveUser (int user_id)
	Remove all feedback by one user More...

void	SaveModel (string filename)
	Save the model parameters to a file More...

string	ToString ()
	Return a string representation of the recommender More...

void	Train ()
	Learn the model parameters of the recommender from the training data More...

void	UpdateRatings (IRatings ratings)
	Update existing ratings and perform incremental training More...

Properties
float	MaxRating `[get, set]`
	Gets or sets the maximum rating. More...

float	MinRating `[get, set]`
	Gets or sets the minimum rating. More...

IRatings	Ratings `[get, set]`
	the ratings to learn from More...

bool	UpdateItems `[get, set]`
	true if items shall be updated when doing incremental updates More...

bool	UpdateUsers `[get, set]`
	true if users shall be updated when doing incremental updates More...

Detailed Description

Interface for rating predictors which support incremental training

By incremental training we mean that after each update, the recommender does not perform a complete re-training using all data, but only a brief update procedure taking into account the update and only a subset of the existing training data.

This interface does not prevent you from doing a complete re-training when implementing a new class. This makes sense e.g. for simple average-based models.

This interface assumes that every user can rate every item only once.

Member Function Documentation

void AddRatings ( IRatings ratings )

Add new ratings and perform incremental training

Parameters

ratings the ratings

Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, ItemAverage, and IncrementalRatingPredictor.

bool CanPredict	(	int	user_id,
		int	item_id
	)

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.

Parameters

user_id	the user ID
item_id	the item ID

Returns: true if a useful prediction can be made, false otherwise

Implemented in Ensemble, ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, Recommender, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

void LoadModel ( string filename )

inherited

Get the model parameters from a file

Parameters

filename the name of the file to read from

Implemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, Recommender, EntityAverage, Ensemble, WeightedEnsemble, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.

float Predict	(	int	user_id,
		int	item_id
	)

inherited

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

Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, Ensemble, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, WeightedEnsemble, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Recommender, Random, Random, and Zero.

IList<Tuple<int, float> > Recommend	(	int	user_id,
		int	n = `-1`,
		ICollection< int >	ignore_items = `null`,
		ICollection< int >	candidate_items = `null`
	)

inherited

Recommend items for a given user

Parameters

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

Returns: a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

void RemoveItem ( int item_id )

inherited

Remove all feedback by one item

Parameters

item_id the item ID

Implemented in BiasedMatrixFactorization, BPRMF, MatrixFactorization, BPRSLIM, LeastSquareSLIM, MF, IncrementalRatingPredictor, IncrementalItemRecommender, MostPopular, and ItemAverage.

void RemoveRatings ( IDataSet ratings )

Remove existing ratings and perform "incremental" training

Parameters

ratings the user and item IDs of the ratings to be removed

Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, IncrementalRatingPredictor, UserAverage, ItemAverage, and GlobalAverage.

void RemoveUser ( int user_id )

inherited

Remove all feedback by one user

Parameters

user_id the user ID

Implemented in BiasedMatrixFactorization, MatrixFactorization, LeastSquareSLIM, MF, MostPopular, IncrementalRatingPredictor, IncrementalItemRecommender, and UserAverage.

void SaveModel ( string filename )

inherited

Save the model parameters to a file

Parameters

filename the name of the file to write to

Implemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, MostPopular, KNN, SLIM, Recommender, MostPopularByAttributes, EntityAverage, Ensemble, WeightedEnsemble, ExternalItemRecommender, ExternalRatingPredictor, GlobalAverage, Constant, Random, Random, and Zero.

string ToString ( )

inherited

Return a string representation of the recommender

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implemented in BPRMF, BiasedMatrixFactorization, SVDPlusPlus, MatrixFactorization, SigmoidCombinedAsymmetricFactorModel, CoClustering, BPRSLIM, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, SigmoidSVDPlusPlus, SocialMF, NaiveBayes, WRMF, KNN, KNN, MostPopular, TimeAwareBaselineWithFrequencies, SoftMarginRankingMF, Recommender, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.

void Train ( )

inherited

Learn the model parameters of the recommender from the training data

Implemented in BiasedMatrixFactorization, TimeAwareBaseline, BPRMF, KNN, MatrixFactorization, KNN, LatentFeatureLogLinearModel, CoClustering, BiPolarSlopeOne, FactorWiseMatrixFactorization, Recommender, BPRSLIM, Ensemble, SlopeOne, UserItemBaseline, LeastSquareSLIM, TimeAwareBaselineWithFrequencies, SVDPlusPlus, GSVDPlusPlus, SLIM, NaiveBayes, SigmoidCombinedAsymmetricFactorModel, MF, MostPopularByAttributes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SigmoidSVDPlusPlus, MostPopular, ExternalItemRecommender, ExternalRatingPredictor, MultiCoreBPRMF, WeightedBPRMF, WeightedEnsemble, Constant, ItemKNN, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.

void UpdateRatings ( IRatings ratings )

Update existing ratings and perform incremental training

Parameters

ratings the ratings

Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, ItemAverage, and IncrementalRatingPredictor.

Property Documentation

float MaxRating

getsetinherited

Gets or sets the maximum rating.

The maximally possible rating

float MinRating

getsetinherited

Gets or sets the minimum rating.

The minimally possible rating

IRatings Ratings

getsetinherited

the ratings to learn from

bool UpdateItems

getsetinherited

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.

bool UpdateUsers

getsetinherited

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.

The documentation for this interface was generated from the following file:

IIncrementalRatingPredictor.cs

Public Member Functions

Properties

Detailed Description

Member Function Documentation

Property Documentation