Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF).
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override void | AddFeedback (ICollection< Tuple< int, int >> feedback) |
| Add positive feedback events and perform incremental training More...
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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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override float | ComputeObjective () |
| Compute approximate loss More...
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override void | Iterate () |
| Perform one iteration of stochastic gradient ascent over the training data More...
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override void | LoadModel (string file) |
| Get the model parameters from a file More...
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override float | Predict (int user_id, int item_id) |
| Predict rating or score for a given user-item combination 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 | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
| Remove all feedback events by the given user-item combinations More...
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override void | RemoveItem (int item_id) |
| Remove all feedback by one item More...
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override void | RemoveUser (int user_id) |
| Remove all feedback by one user More...
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override void | SaveModel (string file) |
| Save the model parameters to a file More...
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IList< Tuple< int, float > > | ScoreItems (IList< int > accessed_items, IList< int > candidate_items) |
| Score a list of items given a list of items that represent a new user 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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override void | AddItem (int item_id) |
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override void | AddUser (int user_id) |
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override void | InitModel () |
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virtual void | IterateWithoutReplacementUniformPair () |
| Iterate over the training data, uniformly sample from user-item pairs without replacement. More...
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virtual void | IterateWithoutReplacementUniformPair (IList< int > indices) |
| Iterate over the training data, uniformly sample from user-item pairs without replacement. More...
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virtual void | IterateWithoutReplacementUniformUser () |
| Iterate over the training data, uniformly sample from users without replacement. More...
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virtual void | IterateWithReplacementUniformPair () |
| Iterate over the training data, uniformly sample from user-item pairs with replacement. More...
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virtual void | IterateWithReplacementUniformUser () |
| Iterate over the training data, uniformly sample from users with replacement. More...
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override void | RetrainItem (int item_id) |
| Retrain the latent factors of a given item More...
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override void | RetrainUser (int user_id) |
| Retrain the latent factors of a given user More...
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virtual void | SampleItemPair (ICollection< int > user_items, out int item_id, out int other_item_id) |
| Sample a pair of items, given a user More...
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virtual bool | SampleOtherItem (int user_id, int item_id, out int other_item_id) |
| Sample another item, given the first one and the user More...
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virtual void | SampleTriple (out int user_id, out int item_id, out int other_item_id) |
| Sample a triple for BPR learning More...
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virtual int | SampleUser () |
| Uniformly sample a user that has viewed at least one and not all items More...
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override void | UpdateFactors (int u, int i, int j, bool update_u, bool update_i, bool update_j) |
| Update latent factors according to the stochastic gradient descent update rule More...
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static System.Random | random |
| Reference to (per-thread) singleton random number generator More...
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float | BiasReg [get, set] |
| Regularization parameter for the bias term More...
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virtual IPosOnlyFeedback | Feedback [get, set] |
| the feedback data to be used for training More...
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double | InitMean [get, set] |
| Mean of the normal distribution used to initialize the latent factors More...
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double | InitStdDev [get, set] |
| Standard deviation of the normal distribution used to initialize the latent factors More...
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float | LearnRate [get, set] |
| Learning rate alpha More...
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int | MaxItemID [get, set] |
| Maximum item ID More...
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int | MaxUserID [get, set] |
| Maximum user ID More...
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uint | NumFactors [get, set] |
| Number of latent factors per user/item More...
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uint | NumIter [get, set] |
| Number of iterations over the training data More...
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float | RegI [get, set] |
| Regularization parameter for positive item factors More...
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float | RegJ [get, set] |
| Regularization parameter for negative item factors More...
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float | RegU [get, set] |
| Regularization parameter for user factors More...
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bool | UniformUserSampling [get, set] |
| Sample uniformly from users More...
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bool | UpdateItems [get, set] |
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bool | UpdateJ [get, set] |
| If set (default), update factors for negative sampled items during learning More...
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bool | UpdateUsers [get, set] |
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bool | WithReplacement [get, set] |
| Sample positive observations with (true) or without (false) replacement More...
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Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF).
Literature:
This recommender supports incremental updates.
override void AddFeedback |
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ICollection< Tuple< int, int >> |
feedback | ) |
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inlinevirtualinherited |
Add positive feedback events and perform incremental training
- Parameters
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feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
virtual bool CanPredict |
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int |
user_id, |
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int |
item_id |
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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 approximate loss
- Returns
- the approximate loss
Reimplemented from BPRMF.
override void Iterate |
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inlinevirtualinherited |
Perform one iteration of stochastic gradient ascent over the training data
One iteration is samples number of positive entries in the training matrix times
Implements MF.
virtual void IterateWithoutReplacementUniformPair |
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inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
Reimplemented in MultiCoreBPRMF.
virtual void IterateWithoutReplacementUniformPair |
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IList< int > |
indices | ) |
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inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
virtual void IterateWithoutReplacementUniformUser |
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inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from users without replacement.
virtual void IterateWithReplacementUniformPair |
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inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from user-item pairs with replacement.
virtual void IterateWithReplacementUniformUser |
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inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from users with replacement.
override void LoadModel |
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string |
filename | ) |
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inlineinherited |
Get the model parameters from a file
- Parameters
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filename | the name of the file to read from |
Implements IRecommender.
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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inlineinherited |
Predict rating or score for a given user-item combination
- Parameters
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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 |
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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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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 RemoveFeedback |
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ICollection< Tuple< int, int >> |
feedback | ) |
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inlinevirtualinherited |
Remove all feedback events by the given user-item combinations
- Parameters
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feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
override void RemoveItem |
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int |
item_id | ) |
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inlinevirtualinherited |
override void RemoveUser |
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int |
user_id | ) |
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inlinevirtualinherited |
override void RetrainItem |
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int |
item_id | ) |
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inlineprotectedvirtualinherited |
Retrain the latent factors of a given item
- Parameters
-
Implements MF.
override void RetrainUser |
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int |
user_id | ) |
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inlineprotectedvirtualinherited |
Retrain the latent factors of a given user
- Parameters
-
Implements MF.
virtual void SampleItemPair |
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ICollection< int > |
user_items, |
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out int |
item_id, |
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out int |
other_item_id |
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) |
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inlineprotectedvirtualinherited |
Sample a pair of items, given a user
- Parameters
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user_items | the items accessed by the given user |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
virtual bool SampleOtherItem |
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int |
user_id, |
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int |
item_id, |
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out int |
other_item_id |
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) |
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inlineprotectedvirtualinherited |
Sample another item, given the first one and the user
- Parameters
-
user_id | the user ID |
item_id | the ID of the given item |
other_item_id | the ID of the other item |
- Returns
- true if the given item was already seen by user u
virtual void SampleTriple |
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out int |
user_id, |
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out int |
item_id, |
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out int |
other_item_id |
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) |
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inlineprotectedvirtualinherited |
Sample a triple for BPR learning
- Parameters
-
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
Reimplemented in WeightedBPRMF.
virtual int SampleUser |
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inlineprotectedvirtualinherited |
Uniformly sample a user that has viewed at least one and not all items
- Returns
- the user ID
override void SaveModel |
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string |
filename | ) |
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inlineinherited |
Save the model parameters to a file
- Parameters
-
filename | the name of the file to write to |
Implements IRecommender.
IList<Tuple<int, float> > ScoreItems |
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IList< int > |
accessed_items, |
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IList< int > |
candidate_items |
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) |
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inlineinherited |
Score a list of items given a list of items that represent a new user
- Returns
- a list of int and float pairs, representing item IDs and predicted scores
- Parameters
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accessed_items | the ratings (item IDs and rating values) representing the new user |
candidate_items | the items to be rated |
Implements IFoldInItemRecommender.
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.
override void UpdateFactors |
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int |
u, |
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int |
i, |
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int |
j, |
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bool |
update_u, |
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bool |
update_i, |
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bool |
update_j |
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inlineprotectedvirtual |
Update latent factors according to the stochastic gradient descent update rule
- Parameters
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u | the user ID |
i | the ID of the first item |
j | the ID of the second item |
update_u | if true, update the user latent factors |
update_i | if true, update the latent factors of the first item |
update_j | if true, update the latent factors of the second item |
Reimplemented from BPRMF.
Latent item factor matrix
Number of latent factors per user/item
Reference to (per-thread) singleton random number generator
Regularization parameter for positive item factors
Regularization parameter for negative item factors
Regularization parameter for user factors
If set (default), update factors for negative sampled items during learning
Latent user factor matrix
Regularization parameter for the bias term
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
Regularization parameter for positive item factors
Regularization parameter for negative item factors
Regularization parameter for user factors
Sample uniformly from users
If set (default), update factors for negative sampled items during learning
Sample positive observations with (true) or without (false) replacement
The documentation for this class was generated from the following file: