UserAverage Class Reference

Uses the average rating value of a user for predictions. More...

Inheritance diagram for UserAverage:
EntityAverage 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 filename)
 Get the model parameters from a file.
override double Predict (int user_id, int item_id)
 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 Member Functions

void Train (IList< int > entity_ids, int max_entity_id)
 Train the recommender according to the given entity type.

Protected Attributes

IList< double > entity_averages = new List<double>()
 The average rating for each entity.
double global_average = 0
 The global average rating (default prediction if there is no data about an entity).
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.
virtual IRatings Ratings [get, set]
 The rating data.
double this [int index] [get]
 return the average rating for a given entity

Detailed Description

Uses the average rating value of a user for predictions.

This recommender does NOT support incremental updates.


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, inherited]

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, inherited]

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, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.

void Train ( IList< int >  entity_ids,
int  max_entity_id 
) [inline, protected, inherited]

Train the recommender according to the given entity type.

Parameters:
entity_ids list of the relevant entity IDs in the training data
max_entity_id the maximum entity ID

Member Data Documentation

IList<double> entity_averages = new List<double>() [protected, inherited]

The average rating for each entity.

double global_average = 0 [protected, inherited]

The global average rating (default prediction if there is no data about an entity).

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.

virtual IRatings Ratings [get, set, inherited]

The rating data.

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

double this[int index] [get, inherited]

return the average rating for a given entity

Parameters:
index the entity index

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