CoClustering Class Reference

Co-clustering for rating prediction. More...

Inheritance diagram for CoClustering:

List of all members.

Public Member Functions
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
	CoClustering ()
	Default constructor.
double	ComputeFit ()
	Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
void	Iterate ()
	Run one iteration (= pass over the training data).
override void	LoadModel (string filename)
	Get the model parameters from a file.
override double	Predict (int u, int i)
	Predict rating or score for a given user-item combination.
override void	SaveModel (string filename)
	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 Attributes
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.
int	NumItemClusters [get, set]
	The number of item clusters.
uint	NumIter [get, set]
	The maximum number of iterations.
int	NumUserClusters [get, set]
	The number of user clusters.
virtual IRatings	Ratings [get, set]
	The rating data.

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Detailed Description

Co-clustering for rating prediction.

Literature:

• Thomas George, Srujana Merugu A Scalable Collaborative Filtering Framework based on Co-clustering. ICDM 2005. http://hercules.ece.utexas.edu/~srujana/papers/icdm05.pdf

This recommender does NOT support incremental updates.

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Constructor & Destructor Documentation

CoClustering

(

)

[inline]

Default constructor.

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Member Function Documentation

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, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone

(

)

[inline, inherited]

create a shallow copy of the object

double ComputeFit

(

)

[inline]

Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.

Returns:

the fit on the training data according to the optimization criterion; -1 if not implemented

Implements IIterativeModel.

void Iterate

(

)

[inline]

Run one iteration (= pass over the training data).

Implements IIterativeModel.

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.

override double Predict	(	int	user_id,
		int	item_id
	)			[inline, virtual]

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 RatingPredictor.

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]

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 RatingPredictor.

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Member Data Documentation

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.

int NumItemClusters [get, set]

The number of item clusters.

uint NumIter [get, set]

The maximum number of iterations.

If the algorithm converges to a stable solution, it will terminate earlier.

Implements IIterativeModel.

int NumUserClusters [get, set]

The number of user clusters.

virtual IRatings Ratings [get, set, inherited]

The rating data.

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

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

• CoClustering.cs