Matrix factorization for BPR on multiple cores More...

Inheritance diagram for MultiCoreBPRMF:

Public Member Functions
override void	AddFeedback (ICollection< Tuple< int, int >> feedback)
	Add positive feedback events 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
	MultiCoreBPRMF ()
	default constructor
override float	Predict (int user_id, int item_id)
	Predict the weight for a given user-item combination
IList< Tuple< int, float > >	Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null)
	Recommend items for a given user
virtual System.Collections.Generic.IList < Tuple< int, float > >	Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null)
override void	RemoveFeedback (ICollection< Tuple< int, int >> feedback)
	Remove all feedback events by the given user-item combinations
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
IList< Tuple< int, float > >	ScoreItems (IList< int > accessed_items, IList< int > candidate_items)
	Score a list of items given a list of items that represent a new user
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)
override void	InitModel ()
override void	IterateWithoutReplacementUniformPair ()
	Iterate over the training data, uniformly sample from user-item pairs without replacement.
virtual void	IterateWithoutReplacementUniformPair (IList< int > indices)
	Iterate over the training data, uniformly sample from user-item pairs without replacement.
virtual void	IterateWithoutReplacementUniformUser ()
	Iterate over the training data, uniformly sample from users without replacement.
virtual void	IterateWithReplacementUniformPair ()
	Iterate over the training data, uniformly sample from user-item pairs with replacement.
virtual void	IterateWithReplacementUniformUser ()
	Iterate over the training data, uniformly sample from users with replacement.
override void	RetrainItem (int item_id)
	Retrain the latent factors of a given item
override void	RetrainUser (int user_id)
	Retrain the latent factors of a given user
virtual void	SampleItemPair (ICollection< int > user_items, out int item_id, out int other_item_id)
	Sample a pair of items, given a user
virtual bool	SampleOtherItem (int user_id, int item_id, out int other_item_id)
	Sample another item, given the first one and the user
virtual void	SampleTriple (out int user_id, out int item_id, out int other_item_id)
	Sample a triple for BPR learning
virtual int	SampleUser ()
	Uniformly sample a user that has viewed at least one and not all items
virtual void	UpdateFactors (int user_id, int item_id, int other_item_id, bool update_u, bool update_i, bool update_j)
	Update latent factors according to the stochastic gradient descent update rule

Protected Attributes
float[]	item_bias
	Item bias terms
Matrix< float >	item_factors
	Latent item factor matrix
float	learn_rate = 0.05f
	Learning rate alpha
int	num_factors = 10
	Number of latent factors per user/item
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

Static Protected Attributes
static System.Random	random
	Reference to (per-thread) singleton random number generator

Properties
float	BiasReg `[get, set]`
	Regularization parameter for the bias term
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	MaxThreads `[get, set]`
	the maximum number of threads to use
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	UpdateItems `[get, set]`
bool	UpdateJ `[get, set]`
	If set (default), update factors for negative sampled items during learning
bool	UpdateUsers `[get, set]`
bool	WithReplacement `[get, set]`
	Sample positive observations with (true) or without (false) replacement

Detailed Description

Matrix factorization for BPR on multiple cores

This recommender supports incremental updates, however they are currently not performed on multiple cores.

Constructor & Destructor Documentation

MultiCoreBPRMF ( )

inline

default constructor

Member Function Documentation

override void AddFeedback ( ICollection< Tuple< int, int >> feedback )

inlinevirtualinherited

Add positive feedback events and perform incremental training

Parameters

feedback collection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)

inlinevirtualinherited

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.

Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( )

inlineinherited

create a shallow copy of the object

override float ComputeObjective ( )

inlinevirtualinherited

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 ( )

inlinevirtualinherited

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 IterateWithoutReplacementUniformPair ( )

inlineprotectedvirtual

Iterate over the training data, uniformly sample from user-item pairs without replacement.

Reimplemented from BPRMF.

virtual void IterateWithoutReplacementUniformPair ( IList< int > indices )

inlineprotectedvirtualinherited

Iterate over the training data, uniformly sample from user-item pairs without replacement.

virtual void IterateWithoutReplacementUniformUser ( )

inlineprotectedvirtualinherited

Iterate over the training data, uniformly sample from users without replacement.

virtual void IterateWithReplacementUniformPair ( )

inlineprotectedvirtualinherited

Iterate over the training data, uniformly sample from user-item pairs with replacement.

virtual void IterateWithReplacementUniformUser ( )

inlineprotectedvirtualinherited

Iterate over the training data, uniformly sample from users with replacement.

override void LoadModel ( string filename )

inlinevirtualinherited

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
	)

inlinevirtualinherited

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.

IList<Tuple<int, float> > Recommend	(	int	user_id,
		int	n = `-1`,
		ICollection< int >	ignore_items = `null`,
		ICollection< int >	candidate_items = `null`
	)

inherited

Recommend items for a given user

Parameters

user_id	the user ID
n	the number of items to recommend, -1 for as many as possible
ignore_items	collection if items that should not be returned; if null, use empty collection
candidate_items	the candidate items to choose from; if null, use all items

Returns: a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void RemoveFeedback ( ICollection< Tuple< int, int >> feedback )

inlinevirtualinherited

Remove all feedback events by the given user-item combinations

Parameters

feedback collection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

override void RemoveItem ( int item_id )

inlinevirtualinherited

Remove all feedback by one item

Parameters

item_id the item ID

Reimplemented from MF.

override void RemoveUser ( int user_id )

inlinevirtualinherited

Remove all feedback by one user

Parameters

user_id the user ID

Reimplemented from IncrementalItemRecommender.

override void RetrainItem ( int item_id )

inlineprotectedvirtualinherited

Retrain the latent factors of a given item

Parameters

item_id the item ID

Implements MF.

override void RetrainUser ( int user_id )

inlineprotectedvirtualinherited

Retrain the latent factors of a given user

Parameters

user_id the user ID

Implements MF.

virtual void SampleItemPair	(	ICollection< int >	user_items,
		out int	item_id,
		out int	other_item_id
	)

inlineprotectedvirtualinherited

Sample a pair of items, given a user

Parameters

user_items	the items accessed by the given user
item_id	the ID of the first item
other_item_id	the ID of the second item

virtual bool SampleOtherItem	(	int	user_id,
		int	item_id,
		out int	other_item_id
	)

inlineprotectedvirtualinherited

Sample another item, given the first one and the user

Parameters

user_id	the user ID
item_id	the ID of the given item
other_item_id	the ID of the other item

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

virtual void SampleTriple	(	out int	user_id,
		out int	item_id,
		out int	other_item_id
	)

inlineprotectedvirtualinherited

Sample a triple for BPR learning

Parameters

user_id	the user ID
item_id	the ID of the first item
other_item_id	the ID of the second item

Reimplemented in WeightedBPRMF.

virtual int SampleUser ( )

inlineprotectedvirtualinherited

Uniformly sample a user that has viewed at least one and not all items

Returns: the user ID

override void SaveModel ( string filename )

inlinevirtualinherited

Save the model parameters to a file

Parameters

filename the name of the file to write to

Reimplemented from MF.

IList<Tuple<int, float> > ScoreItems	(	IList< int >	accessed_items,
		IList< int >	candidate_items
	)

inlineinherited

Score a list of items given a list of items that represent a new user

Returns: a list of int and float pairs, representing item IDs and predicted scores

Parameters

accessed_items	the ratings (item IDs and rating values) representing the new user
candidate_items	the items to be rated

Implements IFoldInItemRecommender.

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.

override void Train ( )

inlinevirtual

Learn the model parameters of the recommender from the training data

Reimplemented from BPRMF.

virtual void UpdateFactors	(	int	user_id,
		int	item_id,
		int	other_item_id,
		bool	update_u,
		bool	update_i,
		bool	update_j
	)

inlineprotectedvirtualinherited

Update latent factors according to the stochastic gradient descent update rule

Parameters

user_id	the user ID
item_id	the ID of the first item
other_item_id	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

float [] item_bias

protectedinherited

Item bias terms

Matrix<float> item_factors

protectedinherited

Latent item factor matrix

float learn_rate = 0.05f

protectedinherited

Learning rate alpha

int num_factors = 10

protectedinherited

Number of latent factors per user/item

System.Random random

staticprotectedinherited

Reference to (per-thread) singleton random number generator

float reg_i = 0.0025f

protectedinherited

Regularization parameter for positive item factors

float reg_j = 0.00025f

protectedinherited

Regularization parameter for negative item factors

float reg_u = 0.0025f

protectedinherited

Regularization parameter for user factors

bool update_j = true

protectedinherited

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

Matrix<float> user_factors

protectedinherited

Latent user factor matrix

Property Documentation

float BiasReg

getsetinherited

Regularization parameter for the bias term

virtual IPosOnlyFeedback Feedback

getsetinherited

the feedback data to be used for training

double InitMean

getsetinherited

Mean of the normal distribution used to initialize the latent factors

double InitStdDev

getsetinherited

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

float LearnRate

getsetinherited

Learning rate alpha

int MaxItemID

getsetinherited

Maximum item ID

int MaxThreads

getset

the maximum number of threads to use

Determines the number of sections the users and items will be divided into.

int MaxUserID

getsetinherited

Maximum user ID

uint NumFactors

getsetinherited

Number of latent factors per user/item

uint NumIter

getsetinherited

Number of iterations over the training data

float RegI

getsetinherited

Regularization parameter for positive item factors

float RegJ

getsetinherited

Regularization parameter for negative item factors

float RegU

getsetinherited

Regularization parameter for user factors

bool UniformUserSampling

getsetinherited

Sample uniformly from users

bool UpdateJ

getsetinherited

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

bool WithReplacement

getsetinherited

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

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

MultiCoreBPRMF.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation