Interface for rating predictors which support incremental training. More...
Public Member Functions | |
void | AddRating (int user_id, int item_id, double rating) |
Add a new rating and perform incremental training. | |
bool | CanPredict (int user_id, int item_id) |
Check whether a useful prediction can be made for a given user-item combination. | |
void | LoadModel (string filename) |
Get the model parameters from a file. | |
double | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination. | |
void | RemoveItem (int item_id) |
Remove an item from the recommender model, and delete all ratings of this item. | |
void | RemoveRating (int user_id, int item_id) |
Remove an existing rating and perform "incremental" training. | |
void | RemoveUser (int user_id) |
Remove a user from the recommender model, and delete all their ratings. | |
void | SaveModel (string filename) |
Save the model parameters to a file. | |
string | ToString () |
Return a string representation of the recommender. | |
void | Train () |
Learn the model parameters of the recommender from the training data. | |
void | UpdateRating (int user_id, int item_id, double rating) |
Update an existing rating and perform incremental training. | |
Properties | |
double | MaxRating [get, set] |
Gets or sets the maximum rating. | |
double | MinRating [get, set] |
Gets or sets the minimum rating. |
Interface for rating predictors which support incremental training.
By incremental training we mean that after each update, the recommender does not perform a complete re-training using all data, but only a brief update procedure taking into account the update and only a subset of the existing training data.
This interface does not prevent you from doing a complete re-training when implementing a new class. This makes sense e.g. for simple average-based models.
This interface assumes that every user can rate every item only once.
void AddRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) |
Add a new rating and perform incremental training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item | |
rating | the rating value |
Implemented in IncrementalRatingPredictor, ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.
bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [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 |
Implemented in Ensemble, ItemRecommender, BiPolarSlopeOne, GlobalAverage, ItemAverage, RatingPredictor, SlopeOne, and UserAverage.
void LoadModel | ( | string | filename | ) | [inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implemented in Ensemble, WeightedEnsemble, BPR_Linear, BPRMF, ItemRecommender, KNN, MF, MostPopular, Random, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, ItemKNN, KNN, MatrixFactorization, RatingPredictor, SlopeOne, and UserItemBaseline.
double Predict | ( | int | user_id, | |
int | item_id | |||
) | [inherited] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implemented in Ensemble, WeightedEnsemble, BPR_Linear, BPRMF, ItemKNN, ItemRecommender, MF, MostPopular, Random, UserKNN, WeightedItemKNN, WeightedUserKNN, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, RatingPredictor, SlopeOne, UserAverage, UserItemBaseline, and UserKNN.
void RemoveItem | ( | int | item_id | ) |
Remove an item from the recommender model, and delete all ratings of this item.
It is up to the recommender implementor whether there should be model updates after this action, both options are valid.
item_id | the ID of the user to be removed |
Implemented in BiasedMatrixFactorization, IncrementalRatingPredictor, and MatrixFactorization.
void RemoveRating | ( | int | user_id, | |
int | item_id | |||
) |
Remove an existing rating and perform "incremental" training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item |
Implemented in IncrementalRatingPredictor, ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.
void RemoveUser | ( | int | user_id | ) |
Remove a user from the recommender model, and delete all their ratings.
It is up to the recommender implementor whether there should be model updates after this action, both options are valid.
user_id | the ID of the user to be removed |
Implemented in BiasedMatrixFactorization, IncrementalRatingPredictor, and MatrixFactorization.
void SaveModel | ( | string | filename | ) | [inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implemented in Ensemble, WeightedEnsemble, BPR_Linear, BPRMF, ItemRecommender, KNN, MF, MostPopular, Random, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, RatingPredictor, SlopeOne, and UserItemBaseline.
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, ItemRecommender, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, WRMF, BiasedMatrixFactorization, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, RatingPredictor, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.
void UpdateRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) |
Update an existing rating and perform incremental training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item | |
rating | the rating value |
Implemented in IncrementalRatingPredictor, ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.
double MaxRating [get, set, inherited] |
double MinRating [get, set, inherited] |