MyMediaLite  3.01
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
ItemKNNPearson Class Reference

Weighted item-based kNN with pearson correlation. More...

Inheritance diagram for ItemKNNPearson:
ItemKNN KNN IItemSimilarityProvider IncrementalRatingPredictor RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings and perform incremental training.
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
float GetItemSimilarity (int item_id1, int item_id2)
 get the similarity between two items
IList< int > GetMostSimilarItems (int item_id, uint n=10)
 get the most similar items
override void LoadModel (string filename)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item.
virtual void RemoveItem (int item_id)
 Remove an item from the recommender model, and delete all ratings of this item.
override void RemoveRatings (IDataSet ratings)
 Remove existing ratings and perform "incremental" training.
virtual void RemoveUser (int user_id)
 Remove a user from the recommender model, and delete all their ratings.
override void SaveModel (string filename)
 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.
override void UpdateRatings (IRatings ratings)
 Update existing ratings and perform incremental training.

Protected Member Functions

override void AddItem (int item_id)
virtual void AddUser (int user_id)
override void RetrainItem (int item_id)
 Retrain model for a given item.

Protected Attributes

UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 }
 underlying baseline predictor
CorrelationMatrix correlation
 Correlation matrix over some kind of entity.
SparseBooleanMatrix data_item
 Matrix indicating which item was rated by which user.
Func< int, IList< int > > GetPositivelyCorrelatedEntities
 Get positively correlated entities.
float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

Properties

uint K [get, set]
 Number of neighbors to take into account for predictions.
int MaxItemID [get, set]
 Maximum item ID.
virtual float MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual float MinRating [get, set]
 Minimum rating value.
uint NumIter [get, set]
 number of iterations used for training the underlying baseline predictor
override IRatings Ratings [set]
 The rating data.
float RegI [get, set]
 regularization constant for the item bias of the underlying baseline predictor
float RegU [get, set]
 regularization constant for the user bias of the underlying baseline predictor
float Shrinkage [get, set]
 shrinkage (regularization) parameter
bool UpdateItems [get, set]
 true if items shall be updated when doing incremental updates
bool UpdateUsers [get, set]
 true if users shall be updated when doing incremental updates

Detailed Description

Weighted item-based kNN with pearson correlation.

This recommender supports incremental updates.


Member Function Documentation

override void AddRatings ( IRatings  ratings) [inline, virtual, inherited]

Add new ratings and perform incremental training.

Parameters:
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

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.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in BiPolarSlopeOne, Constant, SlopeOne, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

float GetItemSimilarity ( int  item_id1,
int  item_id2 
) [inline, inherited]

get the similarity between two items

Returns:
the item similarity; higher means more similar
Parameters:
item_id1the ID of the first item
item_id2the ID of the second item

Implements IItemSimilarityProvider.

IList<int> GetMostSimilarItems ( int  item_id,
uint  n = 10 
) [inline, inherited]

get the most similar items

Returns:
the items most similar to a given item
Parameters:
item_idthe ID of the item
nthe number of similar items to return

Implements IItemSimilarityProvider.

override void LoadModel ( string  filename) [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from KNN.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Predict the rating of a given user for a given item.

If the user or the item are not known to the recommender, a suitable average is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted rating

Implements RatingPredictor.

virtual void RemoveItem ( int  item_id) [inline, virtual, inherited]

Remove an item from the recommender model, and delete all ratings of this item.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
item_idthe ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.

override void RemoveRatings ( IDataSet  ratings) [inline, virtual, inherited]

Remove existing ratings and perform "incremental" training.

Parameters:
ratingsthe user and item IDs of the ratings to be removed

Reimplemented from IncrementalRatingPredictor.

virtual void RemoveUser ( int  user_id) [inline, virtual, inherited]

Remove a user from the recommender model, and delete all their ratings.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
user_idthe ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

override void RetrainItem ( int  item_id) [inline, protected, virtual]

Retrain model for a given item.

Parameters:
item_idthe item ID

Implements ItemKNN.

override void SaveModel ( string  filename) [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from RatingPredictor.

override string ToString ( ) [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 RatingPredictor.

override void UpdateRatings ( IRatings  ratings) [inline, virtual, inherited]

Update existing ratings and perform incremental training.

Parameters:
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 } [protected, inherited]

underlying baseline predictor

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

SparseBooleanMatrix data_item [protected, inherited]

Matrix indicating which item was rated by which user.

Func<int, IList<int> > GetPositivelyCorrelatedEntities [protected, inherited]

Get positively correlated entities.

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

uint K [get, set, inherited]

Number of neighbors to take into account for predictions.

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual float MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual float MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

uint NumIter [get, set, inherited]

number of iterations used for training the underlying baseline predictor

override IRatings Ratings [set, inherited]

The rating data.

Reimplemented from KNN.

float RegI [get, set, inherited]

regularization constant for the item bias of the underlying baseline predictor

float RegU [get, set, inherited]

regularization constant for the user bias of the underlying baseline predictor

float Shrinkage [get, set]

shrinkage (regularization) parameter

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Default should true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRatingPredictor.

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.

Implements IIncrementalRatingPredictor.


The documentation for this class was generated from the following file: