BPR_Linear Class Reference

Linear model optimized for BPR. More...

Inheritance diagram for BPR_Linear:

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
double	ComputeFit ()
	Compute the fit (RMSE) on the training data.
void	Iterate ()
	Perform one iteration of stochastic gradient ascent over the training data. One iteration is iteration_length * number of entries in the training matrix.
override void	LoadModel (string filename)
	Get the model parameters from a file.
override double	Predict (int user_id, int item_id)
	Predict rating or score 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 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
virtual void	AddItem (int item_id)
virtual void	AddUser (int user_id)
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.
Protected Attributes
int	iteration_length = 5
	One iteration is iteration_length * number of entries in the training matrix.
Properties
int	FastSamplingMemoryLimit [get, set]
	Fast sampling memory limit, in MiB.
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
SparseBooleanMatrix	ItemAttributes [get, set]
double	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.
double	Regularization [get, set]
	Regularization parameter.

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Detailed Description

Linear model optimized for BPR.

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

double ComputeFit

(

)

Compute the fit (RMSE) on the training data.

Returns:

the fit (RMSE) on the training data according to the optimization criterion; -1 if not implemented

Implements IIterativeModel.

void Iterate

(

)

Perform one iteration of stochastic gradient ascent over the training data. One iteration is iteration_length * number of entries in the training matrix.

Implements IIterativeModel.

override void LoadModel

(

string

filename

)

[virtual]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Implements ItemRecommender.

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

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 ItemRecommender.

void SampleItemPair	(	int	u,
		out int	i,
		out int	j
	)			[protected]

Sample a pair of items, given a user.

Parameters:

	u	the user ID
	i	the ID of the first item
	j	the ID of the second item

void SampleTriple	(	out int	u,
		out int	i,
		out int	j
	)			[protected]

Sample a triple for BPR learning.

Parameters:

	u	the user ID
	i	the ID of the first item
	j	the ID of the second item

int SampleUser

(

)

[protected]

Sample a user that has viewed at least one and not all items.

Returns:

the user ID

override void SaveModel

(

string

filename

)

[virtual]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements ItemRecommender.

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.

virtual void UpdateFeatures	(	int	u,
		int	i,
		int	j
	)			[protected, virtual]

Modified feature update method that exploits attribute sparsity.

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Member Data Documentation

int iteration_length = 5 [protected]

One iteration is iteration_length * number of entries in the training matrix.

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Property Documentation

int FastSamplingMemoryLimit [get, set]

Fast sampling memory limit, in MiB.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

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

SparseBooleanMatrix ItemAttributes [get, set]

The binary item attributes

Implements IItemAttributeAwareRecommender.

double LearnRate [get, set]

Learning rate alpha.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

int NumItemAttributes [get, set]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

uint NumIter [get, set]

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:

• BPR_Linear.cs