MyMediaLite  3.09
Public Member Functions | Protected Attributes | Properties
CoClustering Class Reference

Co-clustering for rating prediction. More...

Inheritance diagram for CoClustering:
RatingPredictor IIterativeModel Recommender IRatingPredictor IRecommender 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.
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.

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_idthe user ID
item_idthe 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 ( ) [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:
filenamethe 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_idthe user ID
item_idthe 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_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe 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:
filenamethe 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.


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


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.


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