Matrix factorization with factor-wise learning
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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 More...
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Object | Clone () |
| create a shallow copy of the object More...
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float | ComputeObjective () |
| Compute the current optimization objective (usually loss plus regularization term) of the model More...
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| FactorWiseMatrixFactorization () |
| Default constructor More...
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virtual void | Iterate () |
| Run one iteration (= pass over the training data) More...
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override void | LoadModel (string filename) |
| Get the model parameters from a file More...
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override float | Predict (int user_id, int item_id) |
| Predict the rating of a given user for a given item More...
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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 More...
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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) |
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override void | SaveModel (string filename) |
| Save the model parameters to a file More...
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override string | ToString () |
| Return a string representation of the recommender More...
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override void | Train () |
| Learn the model parameters of the recommender from the training data More...
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double | InitMean [get, set] |
| Mean of the normal distribution used to initialize the factors More...
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double | InitStdev [get, set] |
| Standard deviation of the normal distribution used to initialize the factors More...
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int | MaxItemID [get, set] |
| Maximum item ID More...
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virtual float | MaxRating [get, set] |
| Maximum rating value More...
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int | MaxUserID [get, set] |
| Maximum user ID More...
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virtual float | MinRating [get, set] |
| Minimum rating value More...
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uint | NumFactors [get, set] |
| Number of latent factors More...
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uint | NumIter [get, set] |
| Number of iterations (in this case: number of latent factors) More...
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override IRatings | Ratings [set] |
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float | RegI [get, set] |
| regularization constant for the item bias of the underlying baseline predictor More...
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float | RegU [get, set] |
| regularization constant for the user bias of the underlying baseline predictor More...
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virtual double | Sensibility [get, set] |
| Sensibility parameter (stopping criterion for parameter fitting) More...
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virtual double | Shrinkage [get, set] |
| Shrinkage parameter More...
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Matrix factorization with factor-wise learning
Similar to the approach described in Simon Funk's seminal blog post: http://sifter.org/~simon/journal/20061211.html
Literature:
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Robert Bell, Yehuda Koren, Chris Volinsky: Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems, ACM Int. Conference on Knowledge Discovery and Data Mining (KDD'07), 2007.
This recommender does NOT support incremental updates.
virtual bool CanPredict |
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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
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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
float ComputeObjective |
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inline |
Compute the current optimization objective (usually loss plus regularization term) of the model
- Returns
- the current objective; -1 if not implemented
Implements IIterativeModel.
Run one iteration (= pass over the training data)
Implements IIterativeModel.
override void LoadModel |
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string |
filename | ) |
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inlinevirtual |
Get the model parameters from a file
- Parameters
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filename | the name of the file to read from |
Reimplemented from Recommender.
override float Predict |
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int |
user_id, |
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int |
item_id |
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) |
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inlinevirtual |
Predict the rating of a given user for a given item
If the user or the item are not known to the recommender, the global effects prediction is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.
- Parameters
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user_id | the user ID |
item_id | the item ID |
- Returns
- the predicted rating
Implements Recommender.
IList<Tuple<int, float> > Recommend |
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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
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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 SaveModel |
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string |
filename | ) |
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inlinevirtual |
Save the model parameters to a file
- Parameters
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filename | the name of the file to write to |
Reimplemented from Recommender.
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.
Implements IRecommender.
Learn the model parameters of the recommender from the training data
Implements Recommender.
Mean of the normal distribution used to initialize the factors
Standard deviation of the normal distribution used to initialize the factors
Number of iterations (in this case: number of latent factors)
regularization constant for the item bias of the underlying baseline predictor
regularization constant for the user bias of the underlying baseline predictor
virtual double Sensibility |
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getset |
Sensibility parameter (stopping criterion for parameter fitting)
epsilon in the Bell et al. paper
Shrinkage parameter
alpha in the Bell et al. paper
The documentation for this class was generated from the following file:
- FactorWiseMatrixFactorization.cs