Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model More...

Inheritance diagram for KNN:

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...

	KNN ()
	Default constructor More...

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

abstract 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...

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

virtual 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...

Protected Member Functions
virtual void	AddItem (int item_id)

virtual void	AddUser (int user_id)

void	RecomputeNeighbors (ICollection< int > update_entities)

void	ResizeNearestNeighbors (int new_size)
	Resizes the nearest neighbors list if necessary More...

Protected Attributes
IBinaryDataCorrelationMatrix	correlation_matrix
	Correlation matrix over some kind of entity, e.g. users or items More...

uint	k = 80
	The number of neighbors to take into account for prediction More...

IList< IList< int > >	nearest_neighbors
	Precomputed nearest neighbors More...

Properties
float	Alpha `[get, set]`
	Alpha parameter for BidirectionalConditionalProbability More...

BinaryCorrelationType	Correlation `[get, set]`
	The kind of correlation to use More...

abstract IBooleanMatrix	DataMatrix `[get]`
	data matrix to learn the correlation from More...

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

uint	K `[get, set]`
	The number of neighbors to take into account for prediction More...

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

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

float	Q `[get, set]`
	Exponent to be used for transforming the neighbor's weights More...

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

bool	Weighted `[get, set]`
	Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted. More...

Detailed Description

Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model

See also: MyMediaLite.ItemRecommendation.KNN

Constructor & Destructor Documentation

KNN ( )

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 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.

abstract float Predict	(	int	user_id,
		int	item_id
	)

pure virtualinherited

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.

Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.

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.

virtual void RemoveItem ( int item_id )

inlinevirtualinherited

Remove all feedback by one item

Parameters

item_id the item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, MF, and MostPopular.

virtual void RemoveUser ( int user_id )

inlinevirtualinherited

Remove all feedback by one user

Parameters

user_id the user ID

Implements IIncrementalRecommender.

Reimplemented in LeastSquareSLIM, MF, and MostPopular.

void ResizeNearestNeighbors ( int new_size )

inlineprotected

Resizes the nearest neighbors list if necessary

Parameters

new_size the new size

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.

Reimplemented in UserKNN.

Member Data Documentation

IBinaryDataCorrelationMatrix correlation_matrix

protected

Correlation matrix over some kind of entity, e.g. users or items

uint k = 80

protected

The number of neighbors to take into account for prediction

IList<IList<int> > nearest_neighbors

protected

Precomputed nearest neighbors

Property Documentation

float Alpha

getset

Alpha parameter for BidirectionalConditionalProbability

BinaryCorrelationType Correlation

getset

The kind of correlation to use

abstract IBooleanMatrix DataMatrix

getprotected

data matrix to learn the correlation from

virtual IPosOnlyFeedback Feedback

getsetinherited

the feedback data to be used for training

uint K

getset

The number of neighbors to take into account for prediction

int MaxItemID

getsetinherited

Maximum item ID

int MaxUserID

getsetinherited

Maximum user ID

float Q

getset

Exponent to be used for transforming the neighbor's weights

A value of 0 leads to counting of the relevant neighbors. 1 is the usual weighted prediction. Values greater than 1 give higher weight to higher correlated neighbors.

TODO LIT

bool Weighted

getset

Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

TODO add literature reference

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

ItemRecommendation/KNN.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation