MyMediaLite  3.02
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
UserKNN Class Reference

Weighted user-based kNN. More...

Inheritance diagram for UserKNN:
KNN IUserSimilarityProvider IFoldInRatingPredictor IncrementalRatingPredictor IRatingPredictor RatingPredictor IIncrementalRatingPredictor IRecommender IRatingPredictor IRatingPredictor IRecommender IRecommender UserAttributeKNN UserKNNCosine UserKNNPearson

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
IList< int > GetMostSimilarUsers (int user_id, uint n=10)
 get the most similar users
float GetUserSimilarity (int user_id1, int user_id2)
 get the similarity between two users
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.
IList< Pair< int, float > > ScoreItems (IList< Pair< 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.
abstract 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

virtual void AddItem (int item_id)
override void AddUser (int user_id)
abstract IList< float > FoldIn (IList< Pair< int, float >> rated_items)
 Fold in one user, identified by their ratings.
abstract void RetrainUser (int user_id)
 Retrain model for a given user.

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_user
 boolean matrix indicating which user rated which item
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
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 user-based kNN.


Member Function Documentation

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

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

abstract IList<float> FoldIn ( IList< Pair< int, float >>  rated_items) [protected, pure virtual]

Fold in one user, identified by their ratings.

Returns:
a vector containing the similarity with all users
Parameters:
rated_itemsthe ratings to take into account

Implemented in UserAttributeKNN, UserKNNCosine, and UserKNNPearson.

IList<int> GetMostSimilarUsers ( int  user_id,
uint  n = 10 
) [inline]

get the most similar users

Returns:
the users most similar to a given user
Parameters:
user_idthe ID of the user
nthe number of similar users to return

Implements IUserSimilarityProvider.

float GetUserSimilarity ( int  user_id1,
int  user_id2 
) [inline]

get the similarity between two users

Returns:
the user similarity; higher means more similar
Parameters:
user_id1the ID of the first user
user_id2the ID of the second user

Implements IUserSimilarityProvider.

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 RatingPredictor.

Reimplemented in ItemKNN.

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 rating 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.

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]

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.

abstract void RetrainUser ( int  user_id) [protected, pure virtual]

Retrain model for a given user.

Parameters:
user_idthe user ID

Implemented in UserAttributeKNN, UserKNNPearson, and UserKNNCosine.

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.

IList<Pair<int, float> > ScoreItems ( IList< Pair< 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 ( ) [inline, inherited]
override void UpdateRatings ( IRatings  ratings) [inline, virtual]

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.

boolean matrix indicating which user rated which item

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]

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

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: