Linear model optimized for BPR. More...

Inheritance diagram for BPRLinear:

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.
Object	Clone ()
	create a shallow copy of the object
float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model.
void	Iterate ()
	Perform one iteration of stochastic gradient ascent over the training data.
override void	LoadModel (string filename)
	Get the model parameters from a file.
override 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)
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.
Protected Member Functions
void	SampleItemPair (int u, out int i, out int j)
	Sample a pair of items, given a user.
void	SampleTriple (out int u, out int i, out int j)
	Sample a triple for BPR learning.
int	SampleUser ()
	Sample a user that has viewed at least one and not all items.
virtual void	UpdateFeatures (int u, int i, int j)
	Modified feature update method that exploits attribute sparsity.
Properties
virtual IPosOnlyFeedback	Feedback `[get, set]`
	the feedback data to be used for training
double	InitMean `[get, set]`
	mean of the Gaussian distribution used to initialize the features
double	InitStdev `[get, set]`
	standard deviation of the normal distribution used to initialize the features
IBooleanMatrix	ItemAttributes `[get, set]`
float	LearnRate `[get, set]`
	Learning rate alpha.
int	MaxItemID `[get, set]`
	Maximum item ID.
int	MaxUserID `[get, set]`
	Maximum user ID.
int	NumItemAttributes `[get, set]`
uint	NumIter `[get, set]`
	Number of iterations over the training data.
float	Regularization `[get, set]`
	Regularization parameter.

Detailed Description

Linear model optimized for BPR.

Literature:

Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme: Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. ICDM 2011. http://www.ismll.uni-hildesheim.de/pub/pdfs/Gantner_et_al2010Mapping.pdf

This recommender does NOT support incremental updates.

Member Function Documentation

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.

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 ( ) [inline, inherited]

create a shallow copy of the object

float ComputeObjective ( ) [inline]

Compute the current optimization objective (usually loss plus regularization term) of the model.

Returns:: the current objective; -1 if not implemented

Implements IIterativeModel.

void Iterate ( ) [inline]

Perform one iteration of stochastic gradient ascent over the training data.

Implements IIterativeModel.

override void LoadModel ( string filename ) [inline]

Get the model parameters from a file.

Parameters:

filename the name of the file to read from

Implements IRecommender.

override float Predict	(	int	user_id,
		int	item_id
	)		`[inline]`

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.

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