Abstract class that uses an average (by entity) rating value for predictions More...

Inheritance diagram for EntityAverage:

Public Member Functions
virtual void	AddRatings (IRatings new_ratings)
	Add new ratings 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
Object	Clone ()
	create a shallow copy of the object
override void	LoadModel (string filename)
	Get the model parameters from a file
abstract float	Predict (int user_id, int item_id)
	Predict rating or score 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	RemoveItem (int item_id)
	Remove all feedback by one item
virtual void	RemoveRatings (IDataSet ratings_to_delete)
	Remove existing ratings and perform "incremental" training
virtual void	RemoveUser (int user_id)
	Remove all feedback by one user
override void	SaveModel (string filename)
	Save the model parameters to a file
override string	ToString ()
	Return a string representation of the recommender
abstract void	Train ()
	Learn the model parameters of the recommender from the training data
virtual void	UpdateRatings (IRatings new_ratings)
	Update existing ratings and perform incremental training

Protected Member Functions
virtual void	AddItem (int item_id)
virtual void	AddUser (int user_id)
void	Retrain (int entity_id, IList< int > indices)
	Retrain the recommender according to the given entity type
void	Train (IList< int > entity_ids, int max_entity_id)
	Train the recommender according to the given entity type

Protected Attributes
IList< float >	entity_averages
	The average rating for each entity
float	global_average
	The global average rating (default prediction if there is no data about an entity)
float	max_rating
	Maximum rating value
float	min_rating
	Minimum rating value
IRatings	ratings
	rating data

Properties
int	MaxItemID `[get, set]`
	Maximum item ID
virtual float	MaxRating `[get, set]`
	Maximum rating value
int	MaxUserID `[get, set]`
	Maximum user ID
virtual float	MinRating `[get, set]`
	Minimum rating value
virtual IRatings	Ratings `[get, set]`
	The rating data
float	this[int index] `[get]`
	return the average rating for a given entity
bool	UpdateItems `[get, set]`
bool	UpdateUsers `[get, set]`

Detailed Description

Abstract class that uses an average (by entity) rating value for predictions

Member Function Documentation

virtual void AddRatings ( IRatings ratings )

inlinevirtualinherited

Add new ratings and perform incremental training

Parameters

ratings the ratings

Implements IIncrementalRatingPredictor.

Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.

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, BPRLinear, 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