Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for the elastic net loss More...

Inheritance diagram for LeastSquareSLIM:

Public Member Functions
virtual 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...

override float	ComputeObjective ()
	Compute the regularized loss (regularized squared error on training data) More...

void	Iterate (int item_id)
	Perform one iteration of coordinate descent for a given set of item parameters over the training data More...

override void	Iterate ()
	Iterate this instance. More...

	LeastSquareSLIM ()
	Default constructor More...

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

float	Predict (int user_id, int item_id, int exclude_item_id)
	Predict the specified user_id, item_id without taking exclude_item_id into consideration. This is needed for the coordinate descent update rule (equation 5 from Friedman et al. (2010)). More...

override float	Predict (int user_id, int item_id)
	Predict rating or score 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)

virtual 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 filename)
	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...

void	Train (int item_id)
	Learns the set of parameters for a given item More...

Protected Member Functions
override void	AddItem (int item_id)

override void	AddUser (int user_id)

override void	InitModel ()

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

virtual void	UpdateParameters (int item_id, int other_item_id)
	Update item parameters according to the coordinate descent update rule More...

Protected Attributes
Matrix< float >	item_weights
	Item weight matrix (the W matrix in the original paper) More...

ItemKNN	itemKNN
	The item KNN used in the feature selection step More...

uint	neighbors = 50
	How many neighbors to use in the kNN feature selection More...

double	reg_l1 = 0.01
	Regularization parameter for the L1 regularization term (lambda in the original paper) More...

double	reg_l2 = 0.001
	Regularization parameter for the L2 regularization term (beta/2 in the original paper) 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...

uint	K `[get, set]`
	How many neighbors to use in the kNN feature selection More...

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

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

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

double	RegL1 `[get, set]`
	Regularization parameter for the L1 regularization term (lambda in the original paper) More...

double	RegL2 `[get, set]`
	Regularization parameter for the L2 regularization term (beta/2 in the original paper) More...

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

Detailed Description

Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for the elastic net loss

The model is learned using a coordinate descent algorithm with soft thresholding (Friedman et al. 2010).

Literature:

X. Ning, G. Karypis: Slim: Sparse linear methods for top-n recommender systems. ICDM 2011. http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf
J. Friedman, T. Hastie, R. Tibshirani: Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 2010. http://www.jstatsoft.org/v33/i01/paper

This recommender supports incremental updates.

Constructor & Destructor Documentation

LeastSquareSLIM ( )

inline

Default constructor

Member Function Documentation

virtual 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

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, ItemKNN, MostPopular, and MF.

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 ( )

inlinevirtual

Compute the regularized loss (regularized squared error on training data)

Returns: the objective

Implements SLIM.

void Iterate ( int item_id )

inline

Perform one iteration of coordinate descent for a given set of item parameters over the training data

override void Iterate ( )

inlinevirtual

Iterate this instance.

Implements SLIM.

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.

float Predict	(	int	user_id,
		int	item_id,
		int	exclude_item_id
	)

inline

Predict the specified user_id, item_id without taking exclude_item_id into consideration. This is needed for the coordinate descent update rule (equation 5 from Friedman et al. (2010)).

Parameters

user_id	User_id.
item_id	Item_id.
exclude_item_id	Current item ID which shouldn't .

override float Predict	(	int	user_id,
		int	item_id
	)

inlinevirtual

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 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.

virtual 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

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, MostPopular, ItemKNN, and MF.

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.

virtual void RetrainItem ( int item_id )

inlineprotectedvirtual

Retrain the latent factors of a given item

Parameters

item_id the item ID

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 ( )

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.

Implements IRecommender.

override void Train ( )

inlinevirtual

Learn the model parameters of the recommender from the training data

Implements Recommender.

void Train ( int item_id )

inline

Learns the set of parameters for a given item

virtual void UpdateParameters	(	int	item_id,
		int	other_item_id
	)

inlineprotectedvirtual

Update item parameters according to the coordinate descent update rule

Parameters

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

Member Data Documentation

Matrix<float> item_weights

protectedinherited

Item weight matrix (the W matrix in the original paper)

ItemKNN itemKNN

protectedinherited

The item KNN used in the feature selection step

uint neighbors = 50

protected

How many neighbors to use in the kNN feature selection

double reg_l1 = 0.01

protected

Regularization parameter for the L1 regularization term (lambda in the original paper)

double reg_l2 = 0.001

protected

Regularization parameter for the L2 regularization term (beta/2 in the original paper)

Property Documentation

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

uint K

getset

How many neighbors to use in the kNN feature selection

int MaxItemID

getsetinherited

Maximum item ID

int MaxUserID

getsetinherited

Maximum user ID

uint NumIter

getsetinherited

Number of iterations over the training data

double RegL1

getset

Regularization parameter for the L1 regularization term (lambda in the original paper)

double RegL2

getset

Regularization parameter for the L2 regularization term (beta/2 in the original paper)

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

LeastSquareSLIM.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation