MyMediaLite  3.08
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
ItemAttributeKNN Class Reference

Attribute-aware weighted item-based kNN recommender. More...

Inheritance diagram for ItemAttributeKNN:
ItemKNN IItemAttributeAwareRecommender KNN IItemSimilarityProvider IFoldInRatingPredictor IRecommender IncrementalRatingPredictor IRatingPredictor RatingPredictor IIncrementalRatingPredictor IRecommender Recommender IRatingPredictor IRatingPredictor IIncrementalRecommender IRecommender 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.
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.
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)
virtual void RemoveItem (int item_id)
 Remove all feedback by one item.
override void RemoveRatings (IDataSet ratings)
 Remove existing ratings and perform "incremental" training.
virtual void RemoveUser (int user_id)
 Remove all feedback by one user.
override void SaveModel (string filename)
 Save the model parameters to a file.
IList< Tuple< int, float > > ScoreItems (IList< Tuple< int, float >> rated_items, IList< int > candidate_items)
 Rate a list of items given a list of ratings that represent a new user.
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()
 underlying baseline predictor
ICorrelationMatrix correlation_matrix
 Correlation matrix over some kind of entity.
SparseBooleanMatrix data_item
 Matrix indicating which item was rated by which user.
float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

Properties

float Alpha [get, set]
 Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson.
override IBooleanMatrix BinaryDataMatrix [get]
 Return the data matrix that can be used to compute a correlation based on binary data.
RatingCorrelationType Correlation [get, set]
 The kind of correlation to use.
override EntityType Entity [get]
 The entity type of the neighbors used for rating prediction.
IBooleanMatrix ItemAttributes [get, set]
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.
int NumItemAttributes [get, set]
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
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
bool WeightedBinary [get, set]
 If set to true, give a lower weight to evidence coming from very frequent entities.

Detailed Description

Attribute-aware weighted item-based kNN recommender.

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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, 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 Recommender.

override float Predict ( int  user_id,
int  item_id 
) [inline, 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 IRecommender.

IList<Tuple<int, float> > Recommend ( int  user_id,
int  n = -1,
ICollection< int >  ignore_items = null,
ICollection< int >  candidate_items = null 
) [inherited]

Recommend items for a given user.

Parameters:
user_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe candidate items to choose from; if null, use all items
Returns:
a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

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

Remove all feedback by one item.

Parameters:
item_idthe item ID

Implements IIncrementalRecommender.

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 all feedback by one user.

Parameters:
user_idthe user ID

Implements IIncrementalRecommender.

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

Reimplemented from 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 Recommender.

IList<Tuple<int, float> > ScoreItems ( IList< Tuple< int, float >>  rated_items,
IList< int >  candidate_items 
) [inline, inherited]

Rate a list of items given a list of ratings that represent a new user.

Returns:
a list of int and float pairs, representing item IDs and predicted ratings
Parameters:
rated_itemsthe ratings (item IDs and rating values) representing the new user
candidate_itemsthe items to be rated

Implements IFoldInRatingPredictor.

override string ToString ( ) [inline, inherited]

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.

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() [protected, inherited]

underlying baseline predictor

ICorrelationMatrix correlation_matrix [protected, inherited]

Correlation matrix over some kind of entity.

SparseBooleanMatrix data_item [protected, inherited]

Matrix indicating which item was rated by which user.

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

float Alpha [get, set, inherited]

Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson.

override IBooleanMatrix BinaryDataMatrix [get, protected]

Return the data matrix that can be used to compute a correlation based on binary data.

If a purely rating-based correlation is used, this property is ignored.

Reimplemented from ItemKNN.

RatingCorrelationType Correlation [get, set, inherited]

The kind of correlation to use.

override EntityType Entity [get, protected, inherited]

The entity type of the neighbors used for rating prediction.

Reimplemented from KNN.

the binary item attributes

Implements IItemAttributeAwareRecommender.

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.

int NumItemAttributes [get, set]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

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

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

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

Implements IIncrementalRecommender.

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 IIncrementalRecommender.

bool WeightedBinary [get, set, inherited]

If set to true, give a lower weight to evidence coming from very frequent entities.


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