Linear model optimized for BPR
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Public Member Functions |
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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float | ComputeObjective () |
| Compute the current optimization objective (usually loss plus regularization term) of the model
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void | Iterate () |
| Perform one iteration of stochastic gradient ascent over the training data
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override void | LoadModel (string filename) |
| Get the model parameters from a file
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override float | Predict (int user_id, int item_id) |
| Predict rating or score 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 | SaveModel (string filename) |
| 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 |
void | SampleItemPair (int u, out int i, out int j) |
| Sample a pair of items, given a user
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void | SampleTriple (out int u, out int i, out int j) |
| Sample a triple for BPR learning
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int | SampleUser () |
| Sample a user that has viewed at least one and not all items
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virtual void | UpdateFeatures (int u, int i, int j) |
| Modified feature update method that exploits attribute sparsity
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Properties |
virtual IPosOnlyFeedback | Feedback [get, set] |
| the feedback data to be used for training
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double | InitMean [get, set] |
| mean of the Gaussian distribution used to initialize the features
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double | InitStdev [get, set] |
| standard deviation of the normal distribution used to initialize the features
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IBooleanMatrix | ItemAttributes [get, set] |
float | LearnRate [get, set] |
| Learning rate alpha
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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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int | NumItemAttributes [get, set] |
uint | NumIter [get, set] |
| Number of iterations over the training data
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float | Regularization [get, set] |
| Regularization parameter
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Detailed Description
Linear model optimized for BPR
Literature:
This recommender does NOT support incremental updates.
Member Function Documentation
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
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.
Perform one iteration of stochastic gradient ascent over the training data
Implements IIterativeModel.
override void LoadModel |
( |
string |
filename | ) |
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inline |
Get the model parameters from a file
- Parameters
-
filename | the name of the file to read from |
Implements IRecommender.
override float Predict |
( |
int |
user_id, |
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int |
item_id |
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) |
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inline |
Predict rating or score for a given user-item combination
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- the predicted score/rating for the given user-item combination
Implements IRecommender.
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.
void SampleItemPair |
( |
int |
u, |
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out int |
i, |
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out int |
j |
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) |
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inlineprotected |
Sample a pair of items, given a user
- Parameters
-
u | the user ID |
i | the ID of the first item |
j | the ID of the second item |
void SampleTriple |
( |
out int |
u, |
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out int |
i, |
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out int |
j |
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) |
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inlineprotected |
Sample a triple for BPR learning
- Parameters
-
u | the user ID |
i | the ID of the first item |
j | the ID of the second item |
Sample a user that has viewed at least one and not all items
- Returns
- the user ID
override void SaveModel |
( |
string |
filename | ) |
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inline |
Save the model parameters to a file
- Parameters
-
filename | the name of the file to write to |
Implements IRecommender.
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 IRecommender.
virtual void UpdateFeatures |
( |
int |
u, |
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int |
i, |
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int |
j |
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) |
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inlineprotectedvirtual |
Modified feature update method that exploits attribute sparsity
Property Documentation
the feedback data to be used for training
mean of the Gaussian distribution used to initialize the features
standard deviation of the normal distribution used to initialize the features
Number of iterations over the training data
The documentation for this class was generated from the following file: