EntityAverage Class Reference

Abstract class that uses an average (by entity) rating value for predictions. More...

Inheritance diagram for EntityAverage:
RatingPredictor IRatingPredictor IRecommender ItemAverage UserAverage

List of all members.

Public Member Functions

virtual void Add (int user_id, int item_id, double rating)
virtual void AddItem (int item_id)
virtual void AddUser (int user_id)
virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction 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.
abstract double Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
virtual void RemoveItem (int item_id)
virtual void RemoveRating (int user_id, int item_id)
virtual void RemoveUser (int user_id)
override void SaveModel (string file)
 Save the model parameters to a file.
string ToString ()
 Return a string representation of the recommender.
abstract void Train ()
 Learn the model parameters of the recommender from the training data.
virtual void UpdateRating (int user_id, int item_id, double rating)

Protected Member Functions

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

Protected Attributes

List< 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
 The max rating value.
double min_rating
 The min rating value.
IRatings ratings
 rating data

Properties

int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 The max rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 The min rating value.
virtual IRatings Ratings [get, set]
 The rating data.
double this [int index] [get]
 return the average rating for a given entity
bool UpdateItems [get, set]
 true if items shall be updated when doing online updates
bool UpdateUsers [get, set]
 true if users shall be updated when doing online updates

Detailed Description

Abstract class that uses an average (by entity) rating value for predictions.

This engine does NOT support online updates.


Member Function Documentation

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

Check whether a useful prediction can be made for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inherited]

create a shallow copy of the object

virtual void InitModel (  )  [protected, virtual, inherited]

Inits the recommender model.

This method is called by the Train() method. When overriding, please call base.InitModel() to get the functions performed in the base class.

Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, MatrixFactorization, SlopeOne, and UserItemBaseline.

override void LoadModel ( string  filename  )  [virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements RatingPredictor.

abstract double Predict ( int  user_id,
int  item_id 
) [pure virtual, inherited]

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 BiasedMatrixFactorization, BiPolarSlopeOne, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, SlopeOne, UserAverage, UserItemBaseline, and UserKNN.

override void SaveModel ( string  filename  )  [virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements RatingPredictor.

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 BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, MostPopular, Random, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, WRMF, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, GlobalAverage, ItemAttributeKNN, ItemAverage, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, SlopeOne, UserAttributeKNN, UserAverage, UserItemBaseline, UserKNNCosine, and UserKNNPearson.

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

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

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

The average rating for each entity.

double global_average = 0 [protected]

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

double max_rating [protected, inherited]

The max rating value.

double min_rating [protected, inherited]

The min rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

int MaxItemID [get, set, inherited]

Maximum item ID.

Maximum item ID

virtual double MaxRating [get, set, inherited]

The max rating value.

The max rating value

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

Maximum user ID

virtual double MinRating [get, set, inherited]

The min rating value.

The min rating value

Implements IRatingPredictor.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in ItemKNN, and UserKNN.

double this[int index] [get]

return the average rating for a given entity

Parameters:
index the entity index
bool UpdateItems [get, set, inherited]

true if items shall be updated when doing online updates

true if items shall be updated when doing online updates

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing online updates

true if users shall be updated when doing online updates


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