EntityAverage Class Reference

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

Inheritance diagram for EntityAverage:

List of all members.

Public Member Functions
virtual void	AddRating (int user_id, int item_id, double rating)
	Add a new rating 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 double	Predict (int user_id, int item_id)
	Predict rating or score for a given user-item combination.
virtual void	RemoveItem (int item_id)
	Remove an item from the recommender model, and delete all ratings of this item.
virtual void	RemoveRating (int user_id, int item_id)
	Remove an existing rating and perform "incremental" training.
virtual void	RemoveUser (int user_id)
	Remove a user from the recommender model, and delete all their ratings.
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	UpdateRating (int user_id, int item_id, double rating)
	Update an existing rating 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, IList< int > entity_ids)
	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< double >	entity_averages = new List<double>()
	The average rating for each entity.
double	global_average
	The global average rating (default prediction if there is no data about an entity).
double	max_rating
	Maximum rating value.
double	min_rating
	Minimum rating value.
IRatings	ratings
	rating data
Properties
int	MaxItemID [get, set]
	Maximum item ID.
virtual double	MaxRating [get, set]
	Maximum rating value.
int	MaxUserID [get, set]
	Maximum user ID.
virtual double	MinRating [get, set]
	Minimum rating value.
virtual IRatings	Ratings [get, set]
	The rating data.
double	this [int index] [get]
	return the average rating for a given entity
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

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Detailed Description

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

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Member Function Documentation

virtual void AddRating	(	int	user_id,
		int	item_id,
		double	rating
	)			[inline, virtual, inherited]

Add a new rating and perform incremental training.

Parameters:

	user_id	the ID of the user who performed the rating
	item_id	the ID of the rated item
	rating	the rating value

Implements IIncrementalRatingPredictor.

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

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 BiPolarSlopeOne, Constant, GlobalAverage, ItemAverage, Random, SlopeOne, and UserAverage.

Object Clone

(

)

[inline, inherited]

create a shallow copy of the object

override void LoadModel

(

string

filename

)

[inline, virtual]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Reimplemented from RatingPredictor.

abstract double Predict	(	int	user_id,
		int	item_id
	)			[pure virtual, inherited]

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 BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, Random, SlopeOne, TimeAwareBaseline, UserAverage, UserItemBaseline, and UserKNN.

virtual void RemoveItem

(

int

item_id

)

[inline, virtual, inherited]

Remove an item from the recommender model, and delete all ratings of this item.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:

item_id

the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, ItemAverage, and MatrixFactorization.

virtual void RemoveRating	(	int	user_id,
		int	item_id
	)			[inline, virtual, inherited]

Remove an existing rating and perform "incremental" training.

Parameters:

	user_id	the ID of the user who performed the rating
	item_id	the ID of the rated item

Implements IIncrementalRatingPredictor.

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

virtual void RemoveUser

(

int

user_id

)

[inline, virtual, inherited]

Remove a user from the recommender model, and delete all their ratings.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:

user_id

the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

void Retrain	(	int	entity_id,
		IList< int >	indices,
		IList< int >	entity_ids
	)			[inline, protected]

Retrain the recommender according to the given entity type.

Parameters:

	entity_id	the ID of the entity to update
	indices	list of indices to use for retraining
	entity_ids	list of all entity IDs in the training data (per rating)

override void SaveModel

(

string

filename

)

[inline, virtual]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Reimplemented from RatingPredictor.

override string ToString

(

)

[inline, inherited]

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.

Reimplemented in BiasedMatrixFactorization, CoClustering, Constant, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, LogisticRegressionMatrixFactorization, MatrixFactorization, MultiCoreMatrixFactorization, SocialMF, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.

void Train	(	IList< int >	entity_ids,
		int	max_entity_id
	)			[inline, protected]

Train the recommender according to the given entity type.

Parameters:

	entity_ids	list of all entity IDs in the training data (per rating)
	max_entity_id	the maximum entity ID

virtual void UpdateRating	(	int	user_id,
		int	item_id,
		double	rating
	)			[inline, virtual, inherited]

Update an existing rating and perform incremental training.

Parameters:

	user_id	the ID of the user who performed the rating
	item_id	the ID of the rated item
	rating	the rating value

Implements IIncrementalRatingPredictor.

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

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Member Data Documentation

IList<double> entity_averages = new List<double>() [protected]

The average rating for each entity.

double global_average [protected]

The global average rating (default prediction if there is no data about an entity).

double max_rating [protected, inherited]

Maximum rating value.

double min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data

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Property Documentation

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual double MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.

double this[int index] [get]

return the average rating for a given entity

Parameters:

index

the entity index

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Default should true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRatingPredictor.

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 IIncrementalRatingPredictor.

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

• EntityAverage.cs