UserKNNCosine Class Reference

Weighted user-based kNN with cosine similarity. More...

Inheritance diagram for UserKNNCosine:
UserKNN KNN UserItemBaseline IncrementalRatingPredictor IIterativeModel RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRating (int user_id, int item_id, double rating)
 Add a new rating and perform incremental training.
virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction can be made for a given user-item combination.
Object Clone ()
 create a shallow copy of the object
double ComputeFit ()
 Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
void Iterate ()
 Run one iteration (= pass over the training data).
override void LoadModel (string filename)
 Get the model parameters from a file.
override double 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 RemoveRating (int user_id, int item_id)
 Remove an existing rating 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 UpdateRating (int user_id, int item_id, double rating)
 Update an existing rating and perform incremental training.

Protected Member Functions

override void AddItem (int item_id)
override void AddUser (int user_id)
virtual void RetrainItem (int item_id)
override void RetrainUser (int user_id)

Protected Attributes

CorrelationMatrix correlation
 Correlation matrix over some kind of entity.
SparseBooleanMatrix data_user
 boolean matrix indicating which user rated which item
double global_average
 the global rating average
double[] item_biases
 the item biases
double max_rating
 Maximum rating value.
double min_rating
 Minimum rating value.
IRatings ratings
 rating data
double[] user_biases
 the user biases

Properties

uint K [get, set]
 Number of neighbors to take into account for predictions.
int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 Minimum rating value.
uint NumIter [get, set]
 Number of iterations to run the training.
override IRatings Ratings [set]
 The rating data.
double RegI [get, set]
 Regularization parameter for the item biases.
double RegU [get, set]
 Regularization parameter for the user biases.
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 with cosine similarity.

This recommender supports incremental updates.


Member Function Documentation

override void AddRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Add a new rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Reimplemented from UserItemBaseline.

virtual bool CanPredict ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Check whether a useful prediction can be made for a given user-item combination.

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 BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

double ComputeFit (  )  [inline, inherited]

Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.

Returns:
the fit on the training data according to the optimization criterion; -1 if not implemented

Implements IIterativeModel.

void Iterate (  )  [inline, inherited]

Run one iteration (= pass over the training data).

Implements IIterativeModel.

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

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from UserItemBaseline.

Reimplemented in ItemKNN.

override double 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 rating is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted rating

Reimplemented from UserItemBaseline.

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_id the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, and MatrixFactorization.

override void RemoveRating ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Remove an existing rating and perform "incremental" training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item

Reimplemented from UserItemBaseline.

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_id the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, and MatrixFactorization.

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

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from UserItemBaseline.

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 UserItemBaseline.

override void UpdateRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Update an existing rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Reimplemented from UserItemBaseline.


Member Data Documentation

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

SparseBooleanMatrix data_user [protected, inherited]

boolean matrix indicating which user rated which item

double global_average [protected, inherited]

the global rating average

double [] item_biases [protected, inherited]

the item biases

double max_rating [protected, inherited]

Maximum rating value.

double min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data

double [] user_biases [protected, inherited]

the user biases


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 double MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

uint NumIter [get, set, inherited]

Number of iterations to run the training.

Implements IIterativeModel.

override IRatings Ratings [set, inherited]

The rating data.

Reimplemented from RatingPredictor.

double RegI [get, set, inherited]

Regularization parameter for the item biases.

If not set, the recommender will try to find suitable values.

double RegU [get, set, inherited]

Regularization parameter for the user biases.

If not set, the recommender will try to find suitable values.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

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

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

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


The documentation for this class was generated from the following file:
Generated on Sun Nov 13 20:32:52 2011 for MyMediaLite by  doxygen 1.6.3