Weighted matrix factorization method proposed by Hu et al. and Pan et al.
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Public Member Functions |
override void | AddFeedback (ICollection< Tuple< int, int >> feedback) |
| Add positive feedback events and perform incremental training
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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
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Object | Clone () |
| create a shallow copy of the object
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override float | ComputeObjective () |
| Compute the current optimization objective (usually loss plus regularization term) of the model
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override void | Iterate () |
| Iterate once over the data
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override void | LoadModel (string file) |
| Get the model parameters from a file
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override float | Predict (int user_id, int item_id) |
| Predict the weight for a given user-item combination
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IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
| Recommend items for a given user
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virtual
System.Collections.Generic.IList
< Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
override void | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
| Remove all feedback events by the given user-item combinations
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override void | RemoveItem (int item_id) |
| Remove all feedback by one item
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override void | RemoveUser (int user_id) |
| Remove all feedback by one user
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override void | SaveModel (string file) |
| Save the model parameters to a file
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override string | ToString () |
| Return a string representation of the recommender
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override void | Train () |
| Learn the model parameters of the recommender from the training data
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Protected Member Functions |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
virtual void | InitModel () |
virtual void | Optimize (IBooleanMatrix data, Matrix< float > W, Matrix< float > H) |
| Optimizes the specified data
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override void | RetrainItem (int item_id) |
| Retrain the latent factors of a given item
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override void | RetrainUser (int user_id) |
| Retrain the latent factors of a given user
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Protected Attributes |
Matrix< float > | item_factors |
| Latent item factor matrix
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int | num_factors = 10 |
| Number of latent factors per user/item
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Matrix< float > | user_factors |
| Latent user factor matrix
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Properties |
double | Alpha [get, set] |
| parameter for the weight/confidence that is put on positive observations
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virtual IPosOnlyFeedback | Feedback [get, set] |
| the feedback data to be used for training
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double | InitMean [get, set] |
| Mean of the normal distribution used to initialize the latent factors
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double | InitStdDev [get, set] |
| Standard deviation of the normal distribution used to initialize the latent factors
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int | MaxItemID [get, set] |
| Maximum item ID
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int | MaxUserID [get, set] |
| Maximum user ID
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uint | NumFactors [get, set] |
| Number of latent factors per user/item
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uint | NumIter [get, set] |
| Number of iterations over the training data
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double | Regularization [get, set] |
| Regularization parameter
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bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
Detailed Description
Weighted matrix factorization method proposed by Hu et al. and Pan et al.
We use the fast learning method proposed by Hu et al. (alternating least squares, ALS), and we use a global parameter to give observed values higher weights.
Literature:
This recommender supports incremental updates.
Member Function Documentation
override void AddFeedback |
( |
ICollection< Tuple< int, int >> |
feedback | ) |
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inlinevirtualinherited |
Add positive feedback events and perform incremental training
- Parameters
-
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
virtual bool CanPredict |
( |
int |
user_id, |
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int |
item_id |
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) |
| |
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inlinevirtualinherited |
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.
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- true if a useful prediction can be made, false otherwise
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
create a shallow copy of the object
override float ComputeObjective |
( |
| ) |
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inlinevirtual |
Compute the current optimization objective (usually loss plus regularization term) of the model
- Returns
- the current objective; -1 if not implemented
Implements MF.
override void Iterate |
( |
| ) |
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inlinevirtual |
Iterate once over the data
Implements MF.
override void LoadModel |
( |
string |
filename | ) |
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inlinevirtualinherited |
Get the model parameters from a file
- Parameters
-
filename | the name of the file to read from |
Reimplemented from Recommender.
Reimplemented in BPRMF.
virtual void Optimize |
( |
IBooleanMatrix |
data, |
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|
Matrix< float > |
W, |
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Matrix< float > |
H |
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) |
| |
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inlineprotectedvirtual |
Optimizes the specified data
- Parameters
-
override float Predict |
( |
int |
user_id, |
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int |
item_id |
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) |
| |
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inlinevirtualinherited |
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.
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- the predicted weight
Implements Recommender.
Reimplemented in BPRMF.
IList<Tuple<int, float> > Recommend |
( |
int |
user_id, |
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int |
n = -1 , |
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ICollection< int > |
ignore_items = null , |
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ICollection< int > |
candidate_items = null |
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) |
| |
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inherited |
Recommend items for a given user
- Parameters
-
user_id | the user ID |
n | the number of items to recommend, -1 for as many as possible |
ignore_items | collection if items that should not be returned; if null, use empty collection |
candidate_items | the candidate items to choose from; if null, use all items |
- Returns
- a sorted list of (item_id, score) tuples
Implemented in WeightedEnsemble, and Ensemble.
override void RemoveFeedback |
( |
ICollection< Tuple< int, int >> |
feedback | ) |
|
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inlinevirtualinherited |
Remove all feedback events by the given user-item combinations
- Parameters
-
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
override void RemoveItem |
( |
int |
item_id | ) |
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inlinevirtualinherited |
override void RemoveUser |
( |
int |
user_id | ) |
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inlinevirtualinherited |
override void RetrainItem |
( |
int |
item_id | ) |
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inlineprotectedvirtual |
Retrain the latent factors of a given item
- Parameters
-
Implements MF.
override void RetrainUser |
( |
int |
user_id | ) |
|
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inlineprotectedvirtual |
Retrain the latent factors of a given user
- Parameters
-
Implements MF.
override void SaveModel |
( |
string |
filename | ) |
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inlinevirtualinherited |
Save the model parameters to a file
- Parameters
-
filename | the name of the file to write to |
Reimplemented from Recommender.
Reimplemented in BPRMF.
override string ToString |
( |
| ) |
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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 Recommender.
Member Data Documentation
Matrix<float> item_factors |
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protectedinherited |
Latent item factor matrix
Number of latent factors per user/item
Matrix<float> user_factors |
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protectedinherited |
Latent user factor matrix
Property Documentation
parameter for the weight/confidence that is put on positive observations
the feedback data to be used for training
Mean of the normal distribution used to initialize the latent factors
Standard deviation of the normal distribution used to initialize the latent factors
Number of latent factors per user/item
Number of iterations over the training data
The documentation for this class was generated from the following file: