Ensemble Class Reference

Abtract class for combining several prediction methods. More...

Inheritance diagram for Ensemble:

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
abstract void	LoadModel (string file)
	Get the model parameters from a file.
abstract float	Predict (int user_id, int item_id)
	Predict rating or score for a given user-item combination.
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.
abstract void	SaveModel (string file)
	Save the model parameters to a file.
string	ToString ()
	Return a string representation of the recommender.
virtual void	Train ()
	Learn the model parameters of the recommender from the training data.
Public Attributes
IList< IRecommender >	recommenders = new List<IRecommender>()
	list of recommenders
Properties
float	MaxRating [get, set]
	The max rating value.
float	MinRating [get, set]
	The min rating value.

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Detailed Description

Abtract class for combining several prediction methods.

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Member Function Documentation

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)		[inline, virtual]

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.

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:

filename

the 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_id	the user ID
item_id	the 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_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

Implements IRecommender.

Implemented in WeightedEnsemble.

abstract void SaveModel

(

string

filename

)

[pure virtual]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements IRecommender.

Implemented in WeightedEnsemble.

string ToString

(

)

[inherited]

Return a string representation of the recommender.

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implemented in BPRMF, BiasedMatrixFactorization, BPRSLIM, BPRMF_Mapping, SVDPlusPlus, MatrixFactorization, CoClustering, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, BPRLinear, SigmoidSVDPlusPlus, BPRMF_ItemMapping, SocialMF, BPRMF_UserMapping, NaiveBayes, KNN, KNN, MostPopular, TimeAwareBaselineWithFrequencies, WRMF, MultiCoreBPRMF, BPRMF_ItemMapping_Optimal, CLiMF, SoftMarginRankingMF, BPRMF_ItemMappingSVR, ItemAttributeSVM, Recommender, BPRMF_UserMapping_Optimal, BPRMF_ItemMappingKNN, WeightedBPRMF, and Constant.

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Member Data Documentation

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

list of recommenders

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Property Documentation

float MaxRating [get, set]

The max rating value.

The max rating value

float MinRating [get, set]

The min rating value.

The min rating value

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

• Ensemble.cs