BPRMF Class Reference

Matrix factorization model for item prediction (ranking) optimized using BPR-Opt. More...

Inheritance diagram for BPRMF:

Public Member Functions
override 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 ()
	Compute the fit (AUC on training data).
virtual double	ComputeLoss ()
	Compute approximate loss.
override void	Iterate ()
	Perform one iteration of stochastic gradient ascent over the training 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.
override void	RemoveFeedback (int user_id, int item_id)
override void	RemoveItem (int item_id)
override 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
override void	AddItem (int item_id)
override void	AddUser (int user_id)
void	CheckSampling ()
override void	InitModel ()
virtual void	RetrainItem (int item_id)
	Retrain the latent factors of a given item.
virtual void	RetrainUser (int user_id)
	Retrain the latent factors of a given user.
virtual void	SampleItemPair (int u, out int i, out int j)
	Sample a pair of items, given a user.
virtual bool	SampleOtherItem (int u, int i, out int j)
	Sample another item, given the first one and the user.
virtual void	SampleTriple (out int u, out int i, out int j)
	Sample a triple for BPR learning.
virtual int	SampleUser ()
	Sample a user that has viewed at least one and not all items.
virtual void	UpdateFactors (int u, int i, int j, bool update_u, bool update_i, bool update_j)
	Update latent factors according to the stochastic gradient descent update rule.
Protected Attributes
bool	fast_sampling = false
	Fast, but memory-intensive sampling.
int	fast_sampling_memory_limit = 1024
	Fast sampling memory limit, in MiB.
IList< double >	item_bias
	Item bias terms.
Matrix< double >	item_factors
	Latent item factor matrix.
double	learn_rate = 0.05
	Learning rate alpha.
int	num_factors = 10
	Number of latent factors per user/item.
System.Random	random
	Random number generator.
double	reg_i = 0.0025
	Regularization parameter for positive item factors.
double	reg_j = 0.00025
	Regularization parameter for negative item factors.
double	reg_u = 0.0025
	Regularization parameter for user factors.
Matrix< double >	user_factors
	Latent user factor matrix.
IList< IList< int > >	user_neg_items
	support data structure for fast sampling
IList< IList< int > >	user_pos_items
	support data structure for fast sampling
Properties
double	BiasReg `[get, set]`
	Regularization parameter for the bias term.
bool	BoldDriver `[get, set]`
	Use bold driver heuristics for learning rate adaption.
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 normal distribution used to initialize the latent factors.
double	InitStdev `[get, set]`
	Standard deviation of the normal distribution used to initialize the latent factors.
double	LearnRate `[get, set]`
	Learning rate alpha.
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	RegI `[get, set]`
	Regularization parameter for positive item factors.
double	RegJ `[get, set]`
	Regularization parameter for negative item factors.
double	RegU `[get, set]`
	Regularization parameter for user factors.

Detailed Description

Matrix factorization model for item prediction (ranking) optimized using BPR-Opt.

BPR reduces ranking to pairwise classification.

Literature:

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme: BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle_et_al2009-Bayesian_Personalized_Ranking.pdf

This recommender supports incremental updates.

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]

Compute the fit (AUC on training data).

Returns:: the fit

Implements MF.

virtual double ComputeLoss ( ) [virtual]

Compute approximate loss.

Returns:: the approximate loss

override void Iterate ( ) [virtual]

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

One iteration is samples number of positive entries in the training matrix times

Implements MF.

override void LoadModel ( string filename ) [virtual]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Reimplemented from MF.

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

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

Reimplemented from MF.

virtual void RetrainItem ( int item_id ) [protected, virtual]

Retrain the latent factors of a given item.

Parameters:

item_id

the item ID

virtual void RetrainUser ( int user_id ) [protected, virtual]

Retrain the latent factors of a given user.

Parameters:

user_id

the user ID

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

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

virtual bool SampleOtherItem	(	int	u,
		int	i,
		out int	j
	)			`[protected, virtual]`

Sample another item, given the first one and the user.

Parameters:

	u	the user ID
	i	the ID of the given item
	j	the ID of the other item

Returns:: true if the given item was already seen by user u

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

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

virtual int SampleUser ( ) [protected, virtual]

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

Reimplemented from MF.

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.

Reimplemented from ItemRecommender.

virtual void UpdateFactors	(	int	u,
		int	i,
		int	j,
		bool	update_u,
		bool	update_i,
		bool	update_j
	)			`[protected, virtual]`

Update latent factors according to the stochastic gradient descent update rule.

Parameters:

	u	the user ID
	i	the ID of the first item
	j	the ID of the second item
	update_u	if true, update the user latent factors
	update_i	if true, update the latent factors of the first item
	update_j	if true, update the latent factors of the second item

Member Data Documentation

bool fast_sampling = false [protected]

Fast, but memory-intensive sampling.

int fast_sampling_memory_limit = 1024 [protected]

Fast sampling memory limit, in MiB.

IList<double> item_bias [protected]

Item bias terms.

Matrix<double> item_factors [protected, inherited]

Latent item factor matrix.

double learn_rate = 0.05 [protected]

Learning rate alpha.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

System.Random random [protected]

Random number generator.

double reg_i = 0.0025 [protected]

Regularization parameter for positive item factors.

double reg_j = 0.00025 [protected]

Regularization parameter for negative item factors.

double reg_u = 0.0025 [protected]

Regularization parameter for user factors.

Matrix<double> user_factors [protected, inherited]

Latent user factor matrix.

IList<IList<int> > user_neg_items [protected]

support data structure for fast sampling

IList<IList<int> > user_pos_items [protected]

support data structure for fast sampling

Property Documentation

double BiasReg [get, set]

Regularization parameter for the bias term.

bool BoldDriver [get, set]

Use bold driver heuristics for learning rate adaption.

See Rainer Gemulla, Peter J. Haas, Erik Nijkamp, Yannis Sismanis: Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent 2011

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, 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.

double LearnRate [get, set]

Learning rate alpha.

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 RegI [get, set]

Regularization parameter for positive item factors.

double RegJ [get, set]

Regularization parameter for negative item factors.

double RegU [get, set]

Regularization parameter for user factors.

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

BPRMF.cs

BPRMF Class Reference

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Member Function Documentation

Member Data Documentation

Property Documentation