Uses a constant rating value for prediction More...

Inheritance diagram for Constant:

Public Member Functions
virtual void	AddRatings (IRatings new_ratings)
	Add new ratings and perform incremental training More...

override 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...

	Constant ()
	Default constructor 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)

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

virtual void	RemoveRatings (IDataSet ratings_to_delete)
	Remove existing ratings and perform "incremental" training 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...

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

Protected Member Functions
virtual void	AddItem (int item_id)

virtual void	AddUser (int user_id)

Protected Attributes
float	max_rating
	Maximum rating value More...

float	min_rating
	Minimum rating value More...

IRatings	ratings
	rating data More...

Properties
float	ConstantRating `[get, set]`
	the constant rating 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...

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

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

Detailed Description

Uses a constant rating value for prediction

This recommender supports incremental updates. Updates are just ignored, because the prediction is always the same.

Constructor & Destructor Documentation

Constant ( )

inline

Default constructor

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.

override bool CanPredict	(	int	user_id,
		int	item_id
	)

inlinevirtual

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

Reimplemented from Recommender.

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.

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