baseline method for rating prediction More...
Public Member Functions | |
override void | AddRating (int user_id, int item_id, double rating) |
Add a new rating and perform incremental training. | |
virtual 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 | |
double | ComputeFit () |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data. | |
void | Iterate () |
Run one iteration (= pass over the training data). | |
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. | |
virtual void | RemoveItem (int item_id) |
Remove an item from the recommender model, and delete all ratings of this item. | |
override void | RemoveRating (int user_id, int item_id) |
Remove an existing rating and perform "incremental" training. | |
virtual void | RemoveUser (int user_id) |
Remove a user from the recommender model, and delete all their ratings. | |
virtual void | RetrainItem (int item_id) |
virtual void | RetrainUser (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. | |
override void | Train () |
Learn the model parameters of the recommender from the training data. | |
override void | UpdateRating (int user_id, int item_id, double rating) |
Update an existing rating and perform incremental training. | |
UserItemBaseline () | |
Default constructor. | |
Protected Member Functions | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
Protected Attributes | |
double | global_average |
the global rating average | |
double[] | item_biases |
the item biases | |
double | max_rating |
Maximum rating value. | |
double | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
double[] | user_biases |
the user biases | |
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. | |
uint | NumIter [get, set] |
Number of iterations to run the training. | |
virtual IRatings | Ratings [get, set] |
The rating data. | |
double | RegI [get, set] |
Regularization parameter for the item biases. | |
double | RegU [get, set] |
Regularization parameter for the user biases. | |
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 |
baseline method for rating prediction
Uses the average rating value, plus a regularized user and item bias for prediction.
The method is described in section 2.1 of Yehuda Koren: Factor in the Neighbors: Scalable and Accurate Collaborative Filtering, Transactions on Knowledge Discovery from Data (TKDD), 2009.
One difference is that we support several iterations of alternating optimization, instead of just one.
This recommender supports incremental updates.
UserItemBaseline | ( | ) | [inline] |
Default constructor.
override void AddRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual] |
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 |
Reimplemented from IncrementalRatingPredictor.
virtual bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
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.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
double ComputeFit | ( | ) | [inline] |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
Implements IIterativeModel.
void Iterate | ( | ) | [inline] |
Run one iteration (= pass over the training data).
Implements IIterativeModel.
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
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.
user_id | the user ID | |
item_id | the item ID |
Implements RatingPredictor.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
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 |
Implements IIncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization, and MatrixFactorization.
override void RemoveRating | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual] |
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 |
Reimplemented from IncrementalRatingPredictor.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
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 |
Implements IIncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization, and MatrixFactorization.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from RatingPredictor.
override string ToString | ( | ) | [inline] |
Return a string representation of the recommender.
The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.
Reimplemented from RatingPredictor.
override void UpdateRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual] |
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 |
Reimplemented from IncrementalRatingPredictor.
double global_average [protected] |
the global rating average
double [] item_biases [protected] |
the item biases
double max_rating [protected, inherited] |
Maximum rating value.
double min_rating [protected, inherited] |
Minimum rating value.
double [] user_biases [protected] |
the user biases
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.
uint NumIter [get, set] |
Number of iterations to run the training.
Implements IIterativeModel.
The rating data.
Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.
double RegI [get, set] |
Regularization parameter for the item biases.
double RegU [get, set] |
Regularization parameter for the user biases.
bool UpdateItems [get, set, inherited] |
true if items shall be updated when doing incremental updates
Default should true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.
Implements IIncrementalRatingPredictor.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.
Implements IIncrementalRatingPredictor.