SVD++: Matrix factorization that also takes into account what users have rated More...

Inheritance diagram for SVDPlusPlus:

Public Member Functions
override void	AddRatings (IRatings ratings)
	Add new ratings 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 More...

virtual void	Iterate ()
	Run one iteration (= pass over the training data) More...

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

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

override void	RemoveRatings (IDataSet ratings)
	Remove existing ratings and perform "incremental" training More...

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

virtual void	RetrainItem (int item_id)
	Updates the latent factors of an item More...

override void	RetrainUser (int user_id)
	Updates the latent factors on a user More...

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

IList< Tuple< int, float > >	ScoreItems (IList< Tuple< int, float >> rated_items, IList< int > candidate_items)
	Rate a list of items given a list of ratings that represent a new user More...

	SVDPlusPlus ()
	Default constructor 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...

override void	UpdateRatings (IRatings ratings)
	Update existing ratings and perform incremental training More...

Protected Member Functions
override void	AddItem (int item_id)

override void	AddUser (int user_id)

virtual double	ComputeLoss ()
	Compute the value of the loss function that is currently being optimized (RMSE) More...

override float[]	FoldIn (IList< Tuple< int, float >> rated_items)
	Compute parameters (latent factors) for a user represented by ratings More...

override void	Iterate (IList< int > rating_indices, bool update_user, bool update_item)
	Iterate once over rating data and adjust corresponding factors (stochastic gradient descent) More...

void	PrecomputeFactors (int user_id)
	Precompute the factors for a given user More...

void	PrecomputeUserFactors ()
	Precompute all user factors More...

float	Predict (int user_id, int item_id, bool bound)

override float	Predict (float[] user_vector, int item_id)
	Predict rating for a fold-in user and an item More...

virtual void	UpdateLearnRate ()
	Updates current_learnrate after each epoch More...

Protected Attributes
float	global_bias
	The bias (global average) More...

int[][]	items_rated_by_user
	The items rated by the users More...

float	max_rating
	Maximum rating value More...

float	min_rating
	Minimum rating value More...

IRatings	ratings
	rating data More...

float[]	y_reg
	precomputed regularization terms for the y matrix More...

Properties
IDataSet	AdditionalFeedback `[get, set]`

float	BiasLearnRate `[get, set]`
	bias learn rate More...

float	BiasReg `[get, set]`
	regularization constant for biases More...

float	Decay `[get, set]`
	Multiplicative learn rate decay More...

bool	FrequencyRegularization `[get, set]`
	Regularization based on rating frequency More...

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

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

float	LearnRate `[get, set]`
	Learn rate (update step size) More...

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

virtual float	MaxRating `[get, set]`
	Maximum rating value More...

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

virtual float	MinRating `[get, set]`
	Minimum rating value More...

uint	NumFactors `[get, set]`
	Number of latent factors More...

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

virtual IRatings	Ratings `[get, set]`
	The rating data More...

virtual float	Regularization `[get, set]`
	Regularization parameter More...

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

Detailed Description

SVD++: Matrix factorization that also takes into account what users have rated

Literature:

Yehuda Koren: Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. KDD 2008. http://research.yahoo.com/files/kdd08koren.pdf

Constructor & Destructor Documentation

SVDPlusPlus ( )

inline

Default constructor

Member Function Documentation

override void AddRatings ( IRatings ratings )

inlinevirtualinherited

Add new ratings and perform incremental training

Parameters

ratings the ratings

Reimplemented from IncrementalRatingPredictor.

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

virtual double ComputeLoss ( )

inlineprotectedvirtual

Compute the value of the loss function that is currently being optimized (RMSE)

Returns: the loss

Reimplemented in SigmoidSVDPlusPlus.

override float ComputeObjective ( )

inlinevirtual

Compute the regularized loss

Returns: the regularized loss

Reimplemented from MatrixFactorization.

override float [] FoldIn ( IList< Tuple< int, float >> rated_items )

inlineprotectedvirtual

Compute parameters (latent factors) for a user represented by ratings

Returns: a vector of latent factors

Parameters

rated_items a list of (item ID, rating value) pairs

Reimplemented from MatrixFactorization.

virtual void Iterate ( )

inlinevirtualinherited

Run one iteration (= pass over the training data)

Implements IIterativeModel.

Reimplemented in BiasedMatrixFactorization.

override void Iterate	(	IList< int >	rating_indices,
		bool	update_user,
		bool	update_item
	)

inlineprotectedvirtual

Iterate once over rating data and adjust corresponding factors (stochastic gradient descent)

Parameters

rating_indices	a list of indices pointing to the ratings to iterate over
update_user	true if user factors to be updated
update_item	true if item factors to be updated

Reimplemented from MatrixFactorization.

override void LoadModel ( string filename )

inline

Get the model parameters from a file

Parameters

filename the name of the file to read from

Implements IRecommender.

void PrecomputeFactors ( int user_id )

inlineprotected

Precompute the factors for a given user

Parameters

user_id the ID of the user

void PrecomputeUserFactors ( )

inlineprotected

Precompute all user factors

override float Predict	(	int	user_id,
		int	item_id
	)

inline

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.

override float Predict	(	float[]	user_vector,
		int	item_id
	)

inlineprotectedvirtual

Predict rating for a fold-in user and an item

Parameters

user_vector	a float vector representing the user
item_id	the item ID

Returns: the predicted rating

Reimplemented from MatrixFactorization.

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 RemoveItem ( int item_id )

inlinevirtualinherited

Remove all feedback by one item

Parameters

item_id the item ID

Reimplemented from IncrementalRatingPredictor.

override void RemoveRatings ( IDataSet ratings )

inlinevirtualinherited

Remove existing ratings and perform "incremental" training

Parameters

ratings the user and item IDs of the ratings to be removed

Reimplemented from IncrementalRatingPredictor.

override void RemoveUser ( int user_id )

inlinevirtualinherited

Remove all feedback by one user

Parameters

user_id the user ID

Reimplemented from IncrementalRatingPredictor.

virtual void RetrainItem ( int item_id )

inlinevirtualinherited

Updates the latent factors of an item

Parameters

item_id the item ID

Reimplemented in BiasedMatrixFactorization.

override void RetrainUser ( int user_id )

inlinevirtual

Updates the latent factors on a user

Parameters

user_id the user ID

Reimplemented from MatrixFactorization.

override void SaveModel ( string filename )

inline

Save the model parameters to a file

Parameters

filename the name of the file to write to

Implements IRecommender.

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

inlineinherited

Rate a list of items given a list of ratings that represent a new user

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

Parameters

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

Implements IFoldInRatingPredictor.

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 ( )

inline

Learn the model parameters of the recommender from the training data

Implements IRecommender.

virtual void UpdateLearnRate ( )

inlineprotectedvirtualinherited

Updates current_learnrate after each epoch

Reimplemented in BiasedMatrixFactorization.

override void UpdateRatings ( IRatings ratings )

inlinevirtualinherited

Update existing ratings and perform incremental training

Parameters

ratings the ratings

Reimplemented from IncrementalRatingPredictor.

Member Data Documentation

float global_bias

protectedinherited

The bias (global average)

int [][] items_rated_by_user

protected

The items rated by the users

float max_rating

protectedinherited

Maximum rating value

float min_rating

protectedinherited

Minimum rating value

IRatings ratings

protectedinherited

rating data

float [] y_reg

protected

precomputed regularization terms for the y matrix

Property Documentation

float BiasLearnRate

getset

bias learn rate

float BiasReg

getset

regularization constant for biases

float Decay

getsetinherited

Multiplicative learn rate decay

Applied after each epoch (= pass over the whole dataset)

bool FrequencyRegularization

getset

Regularization based on rating frequency

Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. As described in the paper by Menon and Elkan.

double InitMean

getsetinherited

Mean of the normal distribution used to initialize the factors

double InitStdDev

getsetinherited

Standard deviation of the normal distribution used to initialize the factors

float LearnRate

getsetinherited

Learn rate (update step size)

int MaxItemID

getsetinherited

Maximum item ID

virtual float MaxRating

getsetinherited

Maximum rating value

int MaxUserID

getsetinherited

Maximum user ID

virtual float MinRating

getsetinherited

Minimum rating value

uint NumFactors

getsetinherited

Number of latent factors

uint NumIter

getsetinherited

Number of iterations over the training data

virtual IRatings Ratings

getsetinherited

The rating data

virtual float Regularization

getsetinherited

Regularization parameter

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

SVDPlusPlus.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation