BiPolarSlopeOne Class Reference

Bi-polar frequency-weighted Slope-One rating prediction. More...

Inheritance diagram for BiPolarSlopeOne:
RatingPredictor IRatingPredictor IRecommender

List of all members.

Public Member Functions

override 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
override void LoadModel (string file)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
override void SaveModel (string file)
 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.
virtual IRatings Ratings [get, set]
 The rating data.

Detailed Description

Bi-polar frequency-weighted Slope-One rating prediction.

This recommender does NOT support incremental updates. They would be easy to implement, though.


Member Function Documentation

override 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

Reimplemented from RatingPredictor.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

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 float 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, 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.

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, CoClustering, Constant, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, LatentFeatureLogLinearModel, MatrixFactorization, SigmoidSVDPlusPlus, SocialMF, SVDPlusPlus, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.


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.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.


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