CoClustering Class Reference

Co-clustering for rating prediction. More...

Inheritance diagram for CoClustering:
RatingPredictor IIterativeModel IRatingPredictor IRecommender

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.

Detailed Description

Co-clustering for rating prediction.

Literature:

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 
) [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.


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


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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