Collaborative Less-is-More Filtering Matrix Factorization. More...

Inheritance diagram for CLiMF:

Public Member Functions
virtual 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.
	CLiMF ()
	Default constructor.
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 ()
	Iterate once over the data.
override void	LoadModel (string file)
	Get the model parameters from a file.
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)
virtual void	RemoveFeedback (ICollection< Tuple< int, int >> feedback)
	Remove all feedback events by the given user-item combinations.
virtual void	RemoveItem (int item_id)
	Remove all feedback by one item.
virtual void	RemoveUser (int user_id)
	Remove all feedback by one user.
override void	SaveModel (string file)
	Save the model parameters to a file.
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
virtual void	AddItem (int item_id)
virtual void	AddUser (int user_id)
virtual void	InitModel ()
Protected Attributes
Matrix< float >	item_factors
	Latent item factor matrix.
int	num_factors = 10
	Number of latent factors per user/item.
Matrix< float >	user_factors
	Latent user factor matrix.
Properties
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; step size for gradient descent updates.
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.
uint	NumIter `[get, set]`
	Number of iterations over the training data.
float	Regularization `[get, set]`
	Regularization parameter.
bool	UpdateItems `[get, set]`
	true if items shall be updated when doing incremental updates
bool	UpdateUsers `[get, set]`
	true if users shall be updated when doing incremental updates

Detailed Description

Collaborative Less-is-More Filtering Matrix Factorization.

WARNING: Implementation of this recommender is not finished yet.

Exceptions:

NotImplementedException Is thrown when a requested operation is not implemented for a given type.

Constructor & Destructor Documentation

CLiMF ( ) [inline]

Default constructor.

Member Function Documentation

virtual 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

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, UserKNN, ItemKNN, and MostPopular.

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

override float ComputeObjective ( ) [inline, virtual]

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

Returns:: the current objective; -1 if not implemented

Implements MF.

override void Iterate ( ) [inline, virtual]

Iterate once over the data.

Implements MF.

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 Recommender.

Reimplemented in BPRMF.

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

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.

Reimplemented in BPRMF, BPRMF_ItemMapping, and BPRMF_UserMapping.

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 ) [inline, virtual, inherited]

Remove all feedback events by the given user-item combinations.

Parameters:

feedback collection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, UserKNN, MostPopular, and ItemKNN.

virtual void RemoveItem ( int item_id ) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:

item_id the item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.

virtual void RemoveUser ( int user_id ) [inline, virtual, inherited]

Remove all feedback by one user.

Parameters:

user_id the user ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.

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 Recommender.

Reimplemented in BPRMF.

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 Recommender.

Member Data Documentation

Matrix<float> item_factors [protected, inherited]

Latent item factor matrix.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

Matrix<float> user_factors [protected, inherited]

Latent user factor matrix.

Property Documentation

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.

float LearnRate [get, set]

Learning rate; step size for gradient descent updates.

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.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

float Regularization [get, set]

Regularization parameter.

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 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.

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

CLiMF.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation