Abstract class for matrix factorization based item predictors. More...
Public Member Functions | |
virtual void | AddFeedback (int user_id, int item_id) |
Add a positive feedback event. | |
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 | |
abstract double | ComputeFit () |
Computes the fit (optimization criterion) on the training data. | |
abstract void | Iterate () |
Iterate once over the data. | |
override void | LoadModel (string file) |
Get the model parameters from a file. | |
MF () | |
Default constructor. | |
override double | Predict (int user_id, int item_id) |
Predict the weight for a given user-item combination. | |
virtual void | RemoveFeedback (int user_id, int item_id) |
Remove all feedback events by the given user-item combination. | |
virtual void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
virtual void | RemoveUser (int user_id) |
Remove all feedback by one user. | |
override void | SaveModel (string file) |
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. | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
virtual void | InitModel () |
Protected Attributes | |
Matrix< double > | item_factors |
Latent item factor matrix. | |
int | num_factors = 10 |
Number of latent factors per user/item. | |
Matrix< double > | user_factors |
Latent user factor matrix. | |
Properties | |
virtual IPosOnlyFeedback | Feedback [get, set] |
the feedback data to be used for training | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the latent factors. | |
double | InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the latent factors. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
uint | NumFactors [get, set] |
Number of latent factors per user/item. | |
uint | NumIter [get, set] |
Number of iterations over the training data. |
Abstract class for matrix factorization based item predictors.
MF | ( | ) | [inline] |
Default constructor.
virtual void AddFeedback | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Add a positive feedback event.
user_id | the user ID | |
item_id | the item ID |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, and MostPopular.
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.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
abstract double ComputeFit | ( | ) | [pure virtual] |
Computes the fit (optimization criterion) on the training data.
Implements IIterativeModel.
abstract void Iterate | ( | ) | [pure virtual] |
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements ItemRecommender.
Reimplemented in BPRMF.
override double Predict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual] |
Predict the weight for a given user-item combination.
If the user or the item are not known to the recommender, zero is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.
user_id | the user ID | |
item_id | the item ID |
Implements ItemRecommender.
Reimplemented in BPRMF.
virtual void RemoveFeedback | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Remove all feedback events by the given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, and MostPopular.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, and MostPopular.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, and MostPopular.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements ItemRecommender.
Reimplemented in BPRMF.
override string ToString | ( | ) | [inline, 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 BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, and WRMF.
Matrix<double> item_factors [protected] |
Latent item factor matrix.
int num_factors = 10 [protected] |
Number of latent factors per user/item.
Matrix<double> user_factors [protected] |
Latent user factor matrix.
virtual IPosOnlyFeedback Feedback [get, set, inherited] |
the feedback data to be used for training
double InitMean [get, set] |
Mean of the normal distribution used to initialize the latent factors.
double InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the latent factors.
int MaxItemID [get, set, inherited] |
Maximum item ID.
int MaxUserID [get, set, inherited] |
Maximum user ID.
uint NumFactors [get, set] |
Number of latent factors per user/item.
uint NumIter [get, set] |
Number of iterations over the training data.
Implements IIterativeModel.