WeightedBPRMF Class Reference

Weigthed BPR-MF with frequency-adjusted sampling. More...

Inheritance diagram for WeightedBPRMF:

Public Member Functions
override void	AddFeedback (int user_id, int item_id)
	Add a positive feedback event and perform incremental training.
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
override float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model.
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 float	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)
	Remove all feedback events by the given user-item combination.
override void	RemoveItem (int item_id)
	Remove all feedback by one item.
override void	RemoveUser (int user_id)
	Remove all feedback by one user.
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.
	WeightedBPRMF ()
	Default constructor.
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.
override 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.
float[]	item_bias
	Item bias terms.
Matrix< float >	item_factors
	Latent item factor matrix.
int[]	items
	array of item IDs of positive user-item pairs
double	last_loss = double.NegativeInfinity
	Loss for the last iteration, used by bold driver heuristics.
float	learn_rate = 0.05f
	Learning rate alpha.
int	num_factors = 10
	Number of latent factors per user/item.
System.Random	random
	Random number generator.
float	reg_i = 0.0025f
	Regularization parameter for positive item factors.
float	reg_j = 0.00025f
	Regularization parameter for negative item factors.
float	reg_u = 0.0025f
	Regularization parameter for user factors.
bool	update_j = true
	If set (default), update factors for negative sampled items during learning.
Matrix< float >	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
int[]	users
	array of user IDs of positive user-item pairs
Properties
float	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	InitStdDev `[get, set]`
	Standard deviation of the normal distribution used to initialize the latent factors.
float	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.
float	RegI `[get, set]`
	Regularization parameter for positive item factors.
float	RegJ `[get, set]`
	Regularization parameter for negative item factors.
float	RegU `[get, set]`
	Regularization parameter for user factors.
bool	UniformUserSampling `[get, set]`
	Sample uniformly from users.
bool	UpdateJ `[get, set]`
	If set (default), update factors for negative sampled items during learning.
bool	WithReplacement `[get, set]`
	Sample positive observations with (true) or without (false) replacement.

Detailed Description

Weigthed BPR-MF with frequency-adjusted sampling.

Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Lars Schmidt-Thieme: Bayesian Personalized Ranking for Non-Uniformly Sampled Items. KDD Cup Workshop 2011

Constructor & Destructor Documentation

WeightedBPRMF ( ) [inline]

Default constructor.

Member Function Documentation

override void AddFeedback	(	int	user_id,
		int	item_id
	)			`[inline, virtual, inherited]`

Add a positive feedback event and perform incremental training.

Parameters:

	user_id	the user ID
	item_id	the item ID

Reimplemented from IncrementalItemRecommender.

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.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

override float ComputeObjective ( ) [inline, virtual, inherited]

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

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

Implements MF.

Reimplemented in SoftMarginRankingMF.

override void Iterate ( ) [inline, virtual, inherited]

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.

Reimplemented in MultiCoreBPRMF.

override void LoadModel ( string filename ) [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Reimplemented from MF.

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

Reimplemented from MF.

Reimplemented in BPRMF_ItemMapping, and BPRMF_UserMapping.

override void RemoveFeedback	(	int	user_id,
		int	item_id
	)			`[inline, virtual, inherited]`

Remove all feedback events by the given user-item combination.

Parameters:

	user_id	the user ID
	item_id	the item ID

Reimplemented from IncrementalItemRecommender.

override void RemoveItem ( int item_id ) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:

item_id

the item ID

Reimplemented from IncrementalItemRecommender.

override void RemoveUser ( int user_id ) [inline, virtual, inherited]

Remove all feedback by one user.

Parameters:

user_id

the user ID

Reimplemented from IncrementalItemRecommender.

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

Retrain the latent factors of a given item.

Parameters:

item_id

the item ID

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

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
	)			`[inline, protected, virtual, inherited]`

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
	)			`[inline, protected, virtual, inherited]`

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

override void SampleTriple	(	out int	u,
		out int	i,
		out int	j
	)			`[inline, 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

Reimplemented from BPRMF.

virtual int SampleUser ( ) [inline, protected, virtual, inherited]

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

Returns:: the user ID

override void SaveModel ( string filename ) [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Reimplemented from MF.

override string ToString ( ) [inline]

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 BPRMF.

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

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

Reimplemented in SoftMarginRankingMF.

Member Data Documentation

bool fast_sampling = false [protected, inherited]

Fast, but memory-intensive sampling.

int fast_sampling_memory_limit = 1024 [protected, inherited]

Fast sampling memory limit, in MiB.

float [] item_bias [protected, inherited]

Item bias terms.

Matrix<float> item_factors [protected, inherited]

Latent item factor matrix.

int [] items [protected]

array of item IDs of positive user-item pairs

double last_loss = double.NegativeInfinity [protected, inherited]

Loss for the last iteration, used by bold driver heuristics.

float learn_rate = 0.05f [protected, inherited]

Learning rate alpha.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

System.Random random [protected, inherited]

Random number generator.

float reg_i = 0.0025f [protected, inherited]

Regularization parameter for positive item factors.

float reg_j = 0.00025f [protected, inherited]

Regularization parameter for negative item factors.

float reg_u = 0.0025f [protected, inherited]

Regularization parameter for user factors.

bool update_j = true [protected, inherited]

If set (default), update factors for negative sampled items during learning.

Matrix<float> user_factors [protected, inherited]

Latent user factor matrix.

IList<IList<int> > user_neg_items [protected, inherited]

support data structure for fast sampling

IList<IList<int> > user_pos_items [protected, inherited]

support data structure for fast sampling

int [] users [protected]

array of user IDs of positive user-item pairs

Property Documentation

float BiasReg [get, set, inherited]

Regularization parameter for the bias term.

bool BoldDriver [get, set, inherited]

Use bold driver heuristics for learning rate adaption.

Does not work too well for BPR-MF.

Literature:

Rainer Gemulla, Peter J. Haas, Erik Nijkamp, Yannis Sismanis: Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent. KDD 2011. http://www.mpi-inf.mpg.de/~rgemulla/publications/gemulla11dsgd.pdf

int FastSamplingMemoryLimit [get, set, inherited]

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 InitStdDev [get, set, inherited]

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

float LearnRate [get, set, inherited]

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.

float RegI [get, set, inherited]

Regularization parameter for positive item factors.

float RegJ [get, set, inherited]

Regularization parameter for negative item factors.

float RegU [get, set, inherited]

Regularization parameter for user factors.

bool UniformUserSampling [get, set, inherited]

Sample uniformly from users.

bool UpdateJ [get, set, inherited]

If set (default), update factors for negative sampled items during learning.

bool WithReplacement [get, set, inherited]

Sample positive observations with (true) or without (false) replacement.

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

WeightedBPRMF.cs

WeightedBPRMF Class Reference

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation