Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it. More...

Inheritance diagram for ItemRecommender:

Public Member Functions
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...

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	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...

Properties
virtual IPosOnlyFeedback	Feedback `[get, set]`
	the feedback data to be used for training More...

int	MaxItemID `[get, set]`
	Maximum item ID More...

int	MaxUserID `[get, set]`
	Maximum user ID More...

Detailed Description

Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it.

The data is stored in two sparse matrices: one user-wise (in the rows) and one item-wise.

Positive-only means we only which items a user has accessed/liked, but not which items a user does not like. If there is not data for a specific item, we do not know whether the user has just not yet accessed the item, or whether they really dislike it.

See http://recsyswiki/wiki/Item_recommendation and http://recsyswiki/wiki/Implicit_feedback

Member Function Documentation

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.

Object Clone ( )

inlineinherited

create a shallow copy of the object

virtual void LoadModel ( string filename )

inlinevirtualinherited

Get the model parameters from a file

Parameters

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.

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, 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.

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