baseline method for rating prediction More...
Public Member Functions | |
override 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 | |
double | ComputeFit () |
Compute the fit (RMSE) 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) |
override 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. | |
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) |
UserItemBaseline () | |
Default constructor. | |
Protected Member Functions | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
override void | InitModel () |
Inits the recommender model. | |
virtual void | RetrainItem (int item_id) |
virtual void | RetrainUser (int user_id) |
Protected Attributes | |
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. | |
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 | ( | ) |
Default constructor.
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
double ComputeFit | ( | ) |
Compute the fit (RMSE) on the training data.
Implements IIterativeModel.
override void InitModel | ( | ) | [protected, virtual] |
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 from RatingPredictor.
void Iterate | ( | ) |
Run one iteration (= pass over the training data).
Implements IIterativeModel.
override void LoadModel | ( | string | filename | ) | [virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements RatingPredictor.
override double Predict | ( | int | user_id, | |
int | item_id | |||
) | [virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements RatingPredictor.
override void SaveModel | ( | string | filename | ) | [virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements RatingPredictor.
Reimplemented in KNN.
override string ToString | ( | ) |
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 ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, UserAttributeKNN, UserKNNCosine, and UserKNNPearson.
double max_rating [protected, inherited] |
The max rating value.
double min_rating [protected, inherited] |
The min rating value.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual double MaxRating [get, set, inherited] |
The max rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual double MinRating [get, set, inherited] |
The min rating value.
Implements IRatingPredictor.
uint NumIter [get, set] |
Number of iterations to run the training.
Implements IIterativeModel.
double RegI [get, set] |
Regularization parameter for the item biases.
If not set, the recommender will try to find suitable values.
double RegU [get, set] |
Regularization parameter for the user biases.
If not set, the recommender will try to find suitable values.
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.