Abstract class for matrix factorization based item predictors More...

Inheritance diagram for MF:

Public Member Functions
override void	AddFeedback (ICollection< Tuple< int, int >> feedback)
	Add positive feedback events and perform incremental training More...

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 More...

Object	Clone ()
	create a shallow copy of the object More...

abstract float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model More...

abstract void	Iterate ()
	Iterate once over the data More...

override void	LoadModel (string file)
	Get the model parameters from a file More...

	MF ()
	Default constructor More...

override float	Predict (int user_id, int item_id)
	Predict the weight for a given user-item combination More...

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 More...

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 More...

override void	RemoveItem (int item_id)
	Remove all feedback by one item More...

override void	RemoveUser (int user_id)
	Remove all feedback by one user More...

override void	SaveModel (string file)
	Save the model parameters to a file More...

override string	ToString ()
	Return a string representation of the recommender More...

override void	Train ()
	Learn the model parameters of the recommender from the training data More...

Protected Member Functions
override void	AddItem (int item_id)

override void	AddUser (int user_id)

virtual void	InitModel ()

abstract void	RetrainItem (int item_id)
	Retrain the latent factors of a given item More...

abstract void	RetrainUser (int user_id)
	Retrain the latent factors of a given user More...

Protected Attributes
Matrix< float >	item_factors
	Latent item factor matrix More...

int	num_factors = 10
	Number of latent factors per user/item More...

Matrix< float >	user_factors
	Latent user factor matrix More...

Properties
virtual IPosOnlyFeedback	Feedback `[get, set]`
	the feedback data to be used for training More...

double	InitMean `[get, set]`
	Mean of the normal distribution used to initialize the latent factors More...

double	InitStdDev `[get, set]`
	Standard deviation of the normal distribution used to initialize the latent factors More...

int	MaxItemID `[get, set]`
	Maximum item ID More...

int	MaxUserID `[get, set]`
	Maximum user ID More...

uint	NumFactors `[get, set]`
	Number of latent factors per user/item More...

uint	NumIter `[get, set]`
	Number of iterations over the training data More...

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

Detailed Description

Abstract class for matrix factorization based item predictors

Constructor & Destructor Documentation

MF ( )

inline

Default constructor

Member Function Documentation

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

inlinevirtual

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

abstract float ComputeObjective ( )

pure virtual

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

Returns: the current objective; -1 if not implemented

Implements IIterativeModel.

Implemented in BPRMF, WRMF, and SoftMarginRankingMF.

abstract void Iterate ( )

pure virtual

Iterate once over the data

Implements IIterativeModel.

Implemented in BPRMF, and WRMF.

override void LoadModel ( string filename )

inlinevirtual

Get the model parameters from a file

Parameters

filename the name of the file to read from

Reimplemented from Recommender.

override float Predict	(	int	user_id,
		int	item_id
	)

inlinevirtual

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

Implements Recommender.

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 )

inlinevirtual

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 )

inlinevirtual

Remove all feedback by one item

Parameters

item_id the item ID

Reimplemented from IncrementalItemRecommender.

override void RemoveUser ( int user_id )

inlinevirtual

Remove all feedback by one user

Parameters

user_id the user ID

Reimplemented from IncrementalItemRecommender.

abstract void RetrainItem ( int item_id )

protectedpure virtual

Retrain the latent factors of a given item

Parameters

item_id the item ID

Implemented in BPRMF, and WRMF.

abstract void RetrainUser ( int user_id )

protectedpure virtual

Retrain the latent factors of a given user

Parameters

user_id the user ID

Implemented in BPRMF, and WRMF.

override void SaveModel ( string filename )

inlinevirtual

Save the model parameters to a file

Parameters

filename the name of the file to write to

Reimplemented from Recommender.

override string ToString ( )

inlineinherited

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.

override void Train ( )

inlinevirtual

Learn the model parameters of the recommender from the training data

Implements Recommender.

Reimplemented in MultiCoreBPRMF, and WeightedBPRMF.

Member Data Documentation

Matrix<float> item_factors

protected

Latent item factor matrix

int num_factors = 10

protected

Number of latent factors per user/item

Matrix<float> user_factors

protected

Latent user factor matrix

Property Documentation

virtual IPosOnlyFeedback Feedback

getsetinherited

the feedback data to be used for training

double InitMean

getset

Mean of the normal distribution used to initialize the latent factors

double InitStdDev

getset

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

int MaxItemID

getsetinherited

Maximum item ID

int MaxUserID

getsetinherited

Maximum user ID

uint NumFactors

getset

Number of latent factors per user/item

uint NumIter

getset

Number of iterations over the training data

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

MF.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation