IIncrementalRatingPredictor Interface Reference

Interface for rating predictors which support incremental training. More...

Inheritance diagram for IIncrementalRatingPredictor:
IRatingPredictor IRecommender IncrementalRatingPredictor MatrixFactorization UserItemBaseline BiasedMatrixFactorization KNN ItemKNN UserKNN ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson

List of all members.

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.

Detailed Description

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.


Member Function Documentation

void AddRating ( int  user_id,
int  item_id,
double  rating 
)

Add a new rating and perform incremental training.

Parameters:
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.

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

Implemented in Ensemble, ItemRecommender, BiPolarSlopeOne, GlobalAverage, ItemAverage, RatingPredictor, SlopeOne, and UserAverage.

void LoadModel ( string  filename  )  [inherited]
double Predict ( int  user_id,
int  item_id 
) [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

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.

Parameters:
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.

Parameters:
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.

Parameters:
user_id the ID of the user to be removed

Implemented in BiasedMatrixFactorization, IncrementalRatingPredictor, and MatrixFactorization.

void SaveModel ( string  filename  )  [inherited]
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.

Parameters:
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.


Property Documentation

double MaxRating [get, set, inherited]

Gets or sets the maximum rating.

The maximally possible rating

Implemented in RatingPredictor.

double MinRating [get, set, inherited]

Gets or sets the minimum rating.

The minimally possible rating

Implemented in RatingPredictor.


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