MF Class Reference

Abstract class for matrix factorization based item predictors. More...

Inheritance diagram for MF:

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
abstract double	ComputeFit ()
	Computes the fit (optimization criterion) on the training data.
abstract void	Iterate ()
	Iterate once over the data.
override void	LoadModel (string file)
	Get the model parameters from a file.
	MF ()
	Default constructor.
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 ()
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
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	InitStdDev [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.

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Detailed Description

Abstract class for matrix factorization based item predictors.

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Constructor & Destructor Documentation

MF

(

)

[inline]

Default constructor.

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Member Function Documentation

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)			[inline, 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

(

)

[inline, inherited]

create a shallow copy of the object

abstract double ComputeFit

(

)

[pure virtual]

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

Returns:

a double representing the fit, lower is better

Implements IIterativeModel.

Implemented in BPRMF, and WRMF.

abstract void Iterate

(

)

[pure virtual]

Iterate once over the data.

Implements IIterativeModel.

Implemented in BPRMF, and WRMF.

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.

Reimplemented in BPRMF.

override double Predict	(	int	user_id,
		int	item_id
	)			[inline]

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 IRecommender.

Reimplemented in BPRMF.

override void SaveModel

(

string

filename

)

[inline]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements IRecommender.

Reimplemented in BPRMF.

override string ToString

(

)

[inline, 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.

Implements IRecommender.

Reimplemented in BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, and WRMF.

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Member Data Documentation

Matrix<double> item_factors [protected]

Latent item factor matrix.

int num_factors = 10 [protected]

Number of latent factors per user/item.

Matrix<double> user_factors [protected]

Latent user factor matrix.

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Property Documentation

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

double InitMean [get, set]

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

double InitStdDev [get, set]

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]

Number of latent factors per user/item.

uint NumIter [get, set]

Number of iterations over the training data.

Implements IIterativeModel.

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

• MF.cs