CoClustering Class Reference

Co-clustering for rating prediction More...

Inheritance diagram for CoClustering:

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 More...
Object	Clone ()
	create a shallow copy of the object More...
	CoClustering ()
	Default constructor More...
float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model More...
void	Iterate ()
	Run one iteration (= pass over the training data) More...
override void	LoadModel (string filename)
	Get the model parameters from a file More...
override float	Predict (int u, int i)
	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)
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...

Protected Attributes
float	max_rating
	Maximum rating value More...
float	min_rating
	Minimum rating value More...
IRatings	ratings
	rating data More...

Properties
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...
int	NumItemClusters [get, set]
	The number of item clusters More...
uint	NumIter [get, set]
	The maximum number of iterations More...
int	NumUserClusters [get, set]
	The number of user clusters More...
virtual IRatings	Ratings [get, set]
	The rating data More...

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.

Constructor & Destructor Documentation

CoClustering ( )

inline

Default constructor

Member Function Documentation

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

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 )

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

Returns

a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void SaveModel ( string  filename )

inlinevirtual

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.

Implements IRecommender.

override void Train ( )

inlinevirtual

Learn the model parameters of the recommender from the training data

Implements Recommender.

Member Data Documentation

float max_rating

protectedinherited

Maximum rating value

float min_rating

protectedinherited

Minimum rating value

IRatings ratings

protectedinherited

rating data

Property Documentation

int MaxItemID

getsetinherited

Maximum item ID

virtual float MaxRating

getsetinherited

Maximum rating value

int MaxUserID

getsetinherited

Maximum user ID

virtual float MinRating

getsetinherited

Minimum rating value

int NumItemClusters

getset

The number of item clusters

uint NumIter

getset

The maximum number of iterations

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

int NumUserClusters

getset

The number of user clusters

virtual IRatings Ratings

getsetinherited

The rating data

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

• CoClustering.cs