WRMF Class Reference

Weighted matrix factorization method proposed by Hu et al. and Pan et al. More...

Inheritance diagram for WRMF:

List of all members.

Public Member Functions
virtual void	AddFeedback (int user_id, int item_id)
virtual bool	CanPredict (int user_id, int item_id)
	Check whether a useful prediction can be made for a given user-item combination.
Object	Clone ()
	create a shallow copy of the object
override double	ComputeFit ()
	Computes the fit (optimization criterion) on the training data.
override void	Iterate ()
	Iterate once over the data.
override void	LoadModel (string file)
	Get the model parameters from a file.
override double	Predict (int user_id, int item_id)
	Predict the weight for a given user-item combination.
virtual void	RemoveFeedback (int user_id, int item_id)
virtual void	RemoveItem (int item_id)
virtual void	RemoveUser (int user_id)
override void	SaveModel (string file)
	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
virtual void	AddItem (int item_id)
virtual void	AddUser (int user_id)
virtual void	InitModel ()
virtual void	Optimize (IBooleanMatrix data, Matrix< double > W, Matrix< double > H)
	Optimizes the specified data.
Protected Attributes
Matrix< double >	item_factors
	Latent item factor matrix.
int	num_factors = 10
	Number of latent factors per user/item.
Matrix< double >	user_factors
	Latent user factor matrix.
Properties
double	CPos [get, set]
	C position: the weight/confidence that is put on positive observations.
virtual IPosOnlyFeedback	Feedback [get, set]
	the feedback data to be used for training
double	InitMean [get, set]
	Mean of the normal distribution used to initialize the latent factors.
double	InitStdev [get, set]
	Standard deviation of the normal distribution used to initialize the latent factors.
int	MaxItemID [get, set]
	Maximum item ID.
int	MaxUserID [get, set]
	Maximum user ID.
uint	NumFactors [get, set]
	Number of latent factors per user/item.
uint	NumIter [get, set]
	Number of iterations over the training data.
double	Regularization [get, set]
	Regularization parameter.

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Detailed Description

Weighted matrix factorization method proposed by Hu et al. and Pan et al.

Y. Hu Y. Koren C. Volinsky: Collaborative filtering for implicit feedback datasets, IEEE International Conference on Data Mining (ICDM), 2008

R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. M. Lukose, M. Scholz, Q. Yang: One-class collaborative filtering, IEEE International Conference on Data Mining (ICDM), 2008

We use the fast computation method proposed by Hu et al. and we use a global weight to penalize observed/unobserved values.

This recommender does NOT support incremental updates.

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Member Function Documentation

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)			[virtual, inherited]

Check whether a useful prediction can be made for a given user-item combination.

Parameters:

	user_id	the user ID
	item_id	the item ID

Returns:

true if a useful prediction can be made, false otherwise

Implements IRecommender.

Object Clone

(

)

[inherited]

create a shallow copy of the object

override double ComputeFit

(

)

[virtual]

Computes the fit (optimization criterion) on the training data.

Returns:

a double representing the fit, lower is better

Implements MF.

override void Iterate

(

)

[virtual]

Iterate once over the data.

Implements MF.

override void LoadModel

(

string

filename

)

[virtual, inherited]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Implements ItemRecommender.

Reimplemented in BPRMF.

virtual void Optimize	(	IBooleanMatrix	data,
		Matrix< double >	W,
		Matrix< double >	H
	)			[protected, virtual]

Optimizes the specified data.

Parameters:

	data	data
	W	W
	H	H

override double Predict	(	int	user_id,
		int	item_id
	)			[virtual, inherited]

Predict the weight for a given user-item combination.

If the user or the item are not known to the recommender, zero is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:

	user_id	the user ID
	item_id	the item ID

Returns:

the predicted weight

Implements ItemRecommender.

Reimplemented in BPRMF.

override void SaveModel

(

string

filename

)

[virtual, inherited]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements ItemRecommender.

Reimplemented in BPRMF.

override string ToString

(

)

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.

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Member Data Documentation

Matrix<double> item_factors [protected, inherited]

Latent item factor matrix.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

Matrix<double> user_factors [protected, inherited]

Latent user factor matrix.

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Property Documentation

double CPos [get, set]

C position: the weight/confidence that is put on positive observations.

The alpha value in Hu et al.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

double InitMean [get, set, inherited]

Mean of the normal distribution used to initialize the latent factors.

double InitStdev [get, set, inherited]

Standard deviation of the normal distribution used to initialize the latent factors.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

uint NumFactors [get, set, inherited]

Number of latent factors per user/item.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

double Regularization [get, set]

Regularization parameter.

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

• WRMF.cs