Abstract class that uses an average (by entity) rating value for predictions. More...
Public Member Functions | |
virtual void | AddRating (int user_id, int item_id, double rating) |
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 filename) |
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 filename) |
Save the model parameters to a file. | |
override 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 | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
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 | |
bool | UpdateItems [get, set] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates |
Abstract class that uses an average (by entity) rating value for predictions.
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.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.
Object Clone | ( | ) | [inherited] |
create a shallow copy of the object
override void LoadModel | ( | string | filename | ) | [virtual] |
Get the model parameters from a file.
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.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, SlopeOne, UserAverage, UserItemBaseline, and UserKNN.
override void SaveModel | ( | string | filename | ) | [virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements RatingPredictor.
override 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.
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.
void Train | ( | IList< int > | entity_ids, | |
int | max_entity_id | |||
) | [protected] |
Train the recommender according to the given entity type.
entity_ids | list of the relevant entity IDs in the training data | |
max_entity_id | the maximum entity ID |
IList<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] |
Maximum rating value.
double min_rating [protected, inherited] |
Minimum rating value.
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.
double this[int index] [get] |
return the average rating for a given entity
index | the entity index |
bool UpdateItems [get, set, inherited] |
true if items shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.