MyMediaLite  3.11
Public Member Functions | Public Attributes | Properties | List of all members
Ensemble Class Referenceabstract

Abtract class for combining several prediction methods More...

Inheritance diagram for Ensemble:
IRecommender WeightedEnsemble

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...
 
abstract void LoadModel (string file)
 Get the model parameters from a file More...
 
abstract float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination More...
 
abstract IList< Tuple< int, float > > Recommend (int user_id, int n=20, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null)
 Recommend items for a given user More...
 
abstract void SaveModel (string file)
 Save the model parameters to a file More...
 
string ToString ()
 Return a string representation of the recommender More...
 
virtual void Train ()
 Learn the model parameters of the recommender from the training data More...
 

Public Attributes

IList< IRecommenderrecommenders = new List<IRecommender>()
 list of recommenders More...
 

Properties

float MaxRating [get, set]
 The max rating value More...
 
float MinRating [get, set]
 The min rating value More...
 

Detailed Description

Abtract class for combining several prediction methods

Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
)
inlinevirtual

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.

Object Clone ( )
inline

create a shallow copy of the object

abstract void LoadModel ( string  filename)
pure virtual

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Implements IRecommender.

Implemented in WeightedEnsemble.

abstract float Predict ( int  user_id,
int  item_id 
)
pure 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 IRecommender.

Implemented in WeightedEnsemble.

abstract IList<Tuple<int, float> > Recommend ( int  user_id,
int  n = 20,
ICollection< int >  ignore_items = null,
ICollection< int >  candidate_items = null 
)
pure virtual

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

Implements IRecommender.

Implemented in WeightedEnsemble.

abstract void SaveModel ( string  filename)
pure virtual

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Implements IRecommender.

Implemented in WeightedEnsemble.

string ToString ( )
inherited
virtual void Train ( )
inlinevirtual

Learn the model parameters of the recommender from the training data

Implements IRecommender.

Reimplemented in WeightedEnsemble.

Member Data Documentation

IList<IRecommender> recommenders = new List<IRecommender>()

list of recommenders

Property Documentation

float MaxRating
getset

The max rating value

The max rating value

float MinRating
getset

The min rating value

The min rating value


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