item attribute to latent factor mapping, optimized for BPR loss More...

Inheritance diagram for BPRMF_ItemMapping_Optimal:

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	IterateMapping ()
	Perform one iteration of the mapping learning process.
override void	LearnAttributeToFactorMapping ()
	Learn the mapping.
override void	LoadModel (string file)
	Get the model parameters from a file.
override float	Predict (int user_id, int item_id)
	Predict rating or score 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 float[]	__MapToLatentFactorSpace (int item_id)
	map to latent factor space (actual function)
override void	AddItem (int item_id)
override void	AddUser (int user_id)
double[]	ComputeMappingFit ()
	Compute the fit of the mapping.
override void	InitModel ()
virtual 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 float[]	MapToLatentFactorSpace (int item_id)
	map to latent factor space (method to be called)
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.
int	SampleItem ()
	Samples an item for the mapping training. Only items that are associated with at least one user are taken into account.
virtual void	SampleItemPair (int user_id, 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.
virtual void	UpdateMappingFactors (int u, int i, int j)
	update the mapping factors for a given user and an item pair
Protected Attributes
Func< int, float[]>	_MapToLatentFactorSpace
	map to latent factor space (field)
Matrix< float >	attribute_to_factor
	The matrix representing the attribute-to-factor mapping.
float[]	factor_bias
	array to store the bias for each mapping
IBooleanMatrix	item_attributes
	The matrix storing the item attributes.
float[]	item_bias
	Item bias terms.
Matrix< float >	item_factors
	Latent item factor matrix.
float	learn_rate = 0.05f
	Learning rate alpha.
double	learn_rate_mapping = 0.01
	The learn rate for training the mapping functions.
int	num_factors = 10
	Number of latent factors per user/item.
int	num_init_mapping = 5
	number of times the regression is computed (to avoid local minima)
int	num_iter_mapping = 10
	number of iterations of the mapping training procedure
float	reg_i = 0.0025f
	Regularization parameter for positive item factors.
float	reg_j = 0.00025f
	Regularization parameter for negative item factors.
double	reg_mapping = 0.1
	regularization constant for the mapping
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.
IBooleanMatrix	ItemAttributes `[get, set]`
float	LearnRate `[get, set]`
	Learning rate alpha.
double	LearnRateMapping `[get, set]`
	The learn rate for training the mapping functions.
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.
int	NumInitMapping `[get, set]`
	number of times the regression is computed (to avoid local minima)
int	NumItemAttributes `[get, set]`
uint	NumIter `[get, set]`
	Number of iterations over the training data.
int	NumIterMapping `[get, set]`
	number of iterations of the mapping training procedure
float	RegI `[get, set]`
	Regularization parameter for positive item factors.
float	RegJ `[get, set]`
	Regularization parameter for negative item factors.
double	RegMapping `[get, set]`
	regularization constant for the mapping
float	RegU `[get, set]`
	Regularization parameter for user factors.
bool	UniformUserSampling `[get, set]`
	Sample uniformly from users.
bool	UpdateItems `[get, set]`
	true if items shall be updated when doing incremental updates
bool	UpdateJ `[get, set]`
	If set (default), update factors for negative sampled items during learning.
bool	UpdateUsers `[get, set]`
	true if users shall be updated when doing incremental updates
bool	WithReplacement `[get, set]`
	Sample positive observations with (true) or without (false) replacement.

Detailed Description

item attribute to latent factor mapping, optimized for BPR loss

Literature:

Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme: Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. ICDM 2011. http://www.ismll.uni-hildesheim.de/pub/pdfs/Gantner_et_al2010Mapping.pdf

This recommender does NOT support incremental updates.

Member Function Documentation

override float [] __MapToLatentFactorSpace ( int item_id ) [inline, protected, virtual]

map to latent factor space (actual function)

Reimplemented from BPRMF_ItemMapping.

override void AddFeedback ( ICollection< Tuple< int, int >> feedback ) [inline, virtual, inherited]

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
	)		`[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.

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

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

double [] ComputeMappingFit ( ) [inline, protected, inherited]

Compute the fit of the mapping.

Returns:: an array of doubles containing the RMSE on the training data for each latent factor

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 BPRMF_Mapping.

override void IterateMapping ( ) [inline, virtual]

Perform one iteration of the mapping learning process.

Reimplemented from BPRMF_ItemMapping.

virtual void IterateWithoutReplacementUniformPair ( ) [inline, protected, virtual, inherited]

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

Reimplemented in MultiCoreBPRMF.

virtual void IterateWithoutReplacementUniformPair ( IList< int > indices ) [inline, protected, virtual, inherited]

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

virtual void IterateWithoutReplacementUniformUser ( ) [inline, protected, virtual, inherited]

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

virtual void IterateWithReplacementUniformPair ( ) [inline, protected, virtual, inherited]

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

virtual void IterateWithReplacementUniformUser ( ) [inline, protected, virtual, inherited]

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

override void LearnAttributeToFactorMapping ( ) [inline, virtual]

Learn the mapping.

Reimplemented from BPRMF_ItemMapping.

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 [] MapToLatentFactorSpace ( int item_id ) [inline, protected, virtual]

map to latent factor space (method to be called)

Reimplemented from BPRMF_ItemMapping.

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

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 IRecommender.

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 ) [inline, virtual, inherited]

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 ) [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

int SampleItem ( ) [inline, protected, inherited]

Samples an item for the mapping training. Only items that are associated with at least one user are taken into account.

Returns:: the item ID

virtual void SampleItemPair	(	int	user_id,
		out int	item_id,
		out int	other_item_id
	)		`[inline, protected, virtual, inherited]`

Sample a pair of items, given a user.

Parameters:

user_id	the user ID
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
	)		`[inline, protected, virtual, inherited]`

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

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 ( ) [inline, protected, virtual, inherited]

Uniformly 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.

IList<Tuple<int, float> > ScoreItems	(	IList< int >	accessed_items,
		IList< int >	candidate_items
	)		`[inline, inherited]`

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_ItemMapping.

virtual void UpdateFactors	(	int	user_id,
		int	item_id,
		int	other_item_id,
		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:

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.

virtual void UpdateMappingFactors	(	int	u,
		int	i,
		int	j
	)		`[inline, protected, virtual]`

update the mapping factors for a given user and an item pair

Parameters:

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

Member Data Documentation

Func<int, float[]> _MapToLatentFactorSpace [protected, inherited]

map to latent factor space (field)

Matrix<float> attribute_to_factor [protected, inherited]

The matrix representing the attribute-to-factor mapping.

includes bias

float [] factor_bias [protected, inherited]

array to store the bias for each mapping

IBooleanMatrix item_attributes [protected, inherited]

The matrix storing the item attributes.

float [] item_bias [protected, inherited]

Item bias terms.

Matrix<float> item_factors [protected, inherited]

Latent item factor matrix.

float learn_rate = 0.05f [protected, inherited]

Learning rate alpha.

double learn_rate_mapping = 0.01 [protected, inherited]

The learn rate for training the mapping functions.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

int num_init_mapping = 5 [protected, inherited]

number of times the regression is computed (to avoid local minima)

may be ignored by the recommender

int num_iter_mapping = 10 [protected, inherited]

number of iterations of the mapping training procedure

System.Random random [static, protected, inherited]

Reference to (per-thread) singleton 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.

double reg_mapping = 0.1 [protected, inherited]

regularization constant for the mapping

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.

Property Documentation

float BiasReg [get, set, inherited]

Regularization parameter for the bias term.

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.

IBooleanMatrix ItemAttributes [get, set, inherited]

the binary item attributes

Implements IItemAttributeAwareRecommender.

float LearnRate [get, set, inherited]

Learning rate alpha.

double LearnRateMapping [get, set, inherited]

The learn rate for training the mapping functions.

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.

int NumInitMapping [get, set, inherited]

number of times the regression is computed (to avoid local minima)

may be ignored by the recommender

int NumItemAttributes [get, set, inherited]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

int NumIterMapping [get, set, inherited]

number of iterations of the mapping training procedure

float RegI [get, set, inherited]

Regularization parameter for positive item factors.

float RegJ [get, set, inherited]

Regularization parameter for negative item factors.

double RegMapping [get, set, inherited]

regularization constant for the mapping

float RegU [get, set, inherited]

Regularization parameter for user factors.

bool UniformUserSampling [get, set, inherited]

Sample uniformly from users.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRecommender.

bool UpdateJ [get, set, inherited]

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

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.

Implements IIncrementalRecommender.

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:

BPRMF_ItemMapping_Optimal.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Static Protected Attributes

Properties

Detailed Description

Member Function Documentation

Member Data Documentation

Property Documentation