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.
float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model.
void	Iterate ()
	Run one iteration (= pass over the training data)
override void	LoadModel (string filename)
	Get the model parameters from a file.
override float	Predict (int u, int i)
	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)
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
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.
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, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone

(

)

[inline, inherited]

create a shallow copy of the object

float ComputeObjective

(

)

[inline]

Compute the current optimization objective (usually loss plus regularization term) of the model.

Returns:

the current objective; -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 Recommender.

override float 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 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

Returns:

a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

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 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.

Reimplemented from Recommender.

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Member Data Documentation

float max_rating [protected, inherited]

Maximum rating value.

float 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 float MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual float 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.

Implements IRatingPredictor.

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

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

• CoClustering.cs