MyMediaLite  3.11
Public Member Functions | Protected Member Functions | Protected Attributes | Properties | List of all members
ItemKNN Class Reference

Weighted item-based kNN More...

Inheritance diagram for ItemKNN:
KNN IItemSimilarityProvider IFoldInRatingPredictor IncrementalRatingPredictor IRatingPredictor RatingPredictor IIncrementalRatingPredictor IRecommender Recommender IRatingPredictor IRatingPredictor IIncrementalRecommender IRecommender IRecommender IRecommender ItemAttributeKNN

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings and perform incremental training More...
 
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...
 
Object Clone ()
 create a shallow copy of the object More...
 
float GetItemSimilarity (int item_id1, int item_id2)
 get the similarity between two items More...
 
IList< int > GetMostSimilarItems (int item_id, uint n=10)
 get the most similar items More...
 
override void LoadModel (string filename)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item More...
 
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...
 
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 More...
 
override void RemoveRatings (IDataSet ratings)
 Remove existing ratings and perform "incremental" training More...
 
virtual void RemoveUser (int user_id)
 Remove all feedback by one user More...
 
override void SaveModel (string filename)
 Save the model parameters to a file More...
 
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 More...
 
override string ToString ()
 Return a string representation of the recommender More...
 
override void Train ()
 Learn the model parameters of the recommender from the training data More...
 
override void UpdateRatings (IRatings ratings)
 Update existing ratings and perform incremental training More...
 

Protected Member Functions

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

Protected Attributes

UserItemBaseline baseline_predictor = new UserItemBaseline()
 underlying baseline predictor More...
 
ICorrelationMatrix correlation_matrix
 Correlation matrix over some kind of entity More...
 
SparseBooleanMatrix data_item
 Matrix indicating which item was rated by which user More...
 
float max_rating
 Maximum rating value More...
 
float min_rating
 Minimum rating value More...
 
IRatings ratings
 rating data More...
 

Properties

float Alpha [get, set]
 Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson More...
 
override IBooleanMatrix BinaryDataMatrix [get]
 
RatingCorrelationType Correlation [get, set]
 The kind of correlation to use More...
 
override EntityType Entity [get]
 
uint K [get, set]
 Number of neighbors to take into account for predictions More...
 
int MaxItemID [get, set]
 Maximum item ID More...
 
virtual float MaxRating [get, set]
 Maximum rating value More...
 
int MaxUserID [get, set]
 Maximum user ID More...
 
virtual float MinRating [get, set]
 Minimum rating value More...
 
uint NumIter [get, set]
 number of iterations used for training the underlying baseline predictor More...
 
override IRatings Ratings [set]
 
float RegI [get, set]
 regularization constant for the item bias of the underlying baseline predictor More...
 
float RegU [get, set]
 regularization constant for the user bias of the underlying baseline predictor More...
 
bool UpdateItems [get, set]
 
bool UpdateUsers [get, set]
 
bool WeightedBinary [get, set]
 If set to true, give a lower weight to evidence coming from very frequent entities More...
 

Detailed Description

Weighted item-based kNN

Member Function Documentation

override void AddRatings ( IRatings  ratings)
inlinevirtual

Add new ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

virtual bool CanPredict ( int  user_id,
int  item_id 
)
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_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 ( )
inlineinherited

create a shallow copy of the object

float GetItemSimilarity ( int  item_id1,
int  item_id2 
)
inline

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

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)
inlinevirtualinherited

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

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)
inlinevirtualinherited

Remove all feedback by one item

Parameters
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.

override void RemoveRatings ( IDataSet  ratings)
inlinevirtual

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)
inlinevirtualinherited

Remove all feedback by one user

Parameters
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

virtual void RetrainItem ( int  item_id)
inlineprotectedvirtual

Retrain model for a given item

Parameters
item_idthe item ID

Reimplemented in ItemAttributeKNN.

override void SaveModel ( string  filename)
inlinevirtualinherited

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

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 ( )
inlineinherited

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.

override void Train ( )
inlinevirtualinherited

Learn the model parameters of the recommender from the training data

Implements Recommender.

override void UpdateRatings ( IRatings  ratings)
inlinevirtual

Update existing ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

Member Data Documentation

UserItemBaseline baseline_predictor = new UserItemBaseline()
protectedinherited

underlying baseline predictor

ICorrelationMatrix correlation_matrix
protectedinherited

Correlation matrix over some kind of entity

SparseBooleanMatrix data_item
protected

Matrix indicating which item was rated by which user

float max_rating
protectedinherited

Maximum rating value

float min_rating
protectedinherited

Minimum rating value

IRatings ratings
protectedinherited

rating data

Property Documentation

float Alpha
getsetinherited

Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson

RatingCorrelationType Correlation
getsetinherited

The kind of correlation to use

uint K
getsetinherited

Number of neighbors to take into account for predictions

int MaxItemID
getsetinherited

Maximum item ID

virtual float MaxRating
getsetinherited

Maximum rating value

int MaxUserID
getsetinherited

Maximum user ID

virtual float MinRating
getsetinherited

Minimum rating value

uint NumIter
getsetinherited

number of iterations used for training the underlying baseline predictor

float RegI
getsetinherited

regularization constant for the item bias of the underlying baseline predictor

float RegU
getsetinherited

regularization constant for the user bias of the underlying baseline predictor

bool WeightedBinary
getsetinherited

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: