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

Baseline method for rating prediction More...

Inheritance diagram for UserItemBaseline:
IncrementalRatingPredictor IIterativeModel RatingPredictor IIncrementalRatingPredictor Recommender IRatingPredictor IRatingPredictor IIncrementalRecommender IRecommender IRecommender IRecommender

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 ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model More...
 
void Iterate ()
 Run one iteration (= pass over the training data) More...
 
override void LoadModel (string filename)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination 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...
 
virtual void RetrainItem (int item_id)
 
virtual void RetrainUser (int user_id)
 
override void SaveModel (string filename)
 Save the model parameters to a file 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...
 
 UserItemBaseline ()
 Default constructor More...
 

Protected Member Functions

override void AddItem (int item_id)
 
override void AddUser (int user_id)
 

Protected Attributes

float global_average
 the global rating average More...
 
float[] item_biases
 the item biases More...
 
float max_rating
 Maximum rating value More...
 
float min_rating
 Minimum rating value More...
 
IRatings ratings
 rating data More...
 
float[] user_biases
 the user biases More...
 

Properties

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]
 
virtual IRatings Ratings [get, set]
 The rating data More...
 
float RegI [get, set]
 Regularization parameter for the item biases More...
 
float RegU [get, set]
 Regularization parameter for the user biases More...
 
bool UpdateItems [get, set]
 
bool UpdateUsers [get, set]
 

Detailed Description

Baseline method for rating prediction

Uses the average rating value, plus a regularized user and item bias for prediction.

The method was described in section 2.1 of the paper below. One difference is that we support several iterations of alternating optimization, instead of just one.

The optimization problem solved by the Train() method is the following:

\[ \min_{\mathbf{a}, \mathbf{b}} \sum_{(u, i, r) \in R} (r - \mu_R - a_u - b_i)^2 + \lambda_1 \|\mathbf{a}\|^2 + \lambda_2 \|\mathbf{b}\|^2, \]

where $R$ are the known ratings, and $\lambda_1$ and $\lambda_2$ are the regularization constants RegU and RegI. The sum represents the least squares error, while the two terms starting with $\lambda_1$ and $\lambda_2$, respectively, are regularization terms that control the parameter sizes to avoid overfitting. The optimization problem is solved an alternating least squares method.

Literature:

This recommender supports incremental updates.

Constructor & Destructor Documentation

UserItemBaseline ( )
inline

Default constructor

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 ComputeObjective ( )
inline

Compute the current optimization objective (usually loss plus regularization term) of the model

Returns
the current objective; -1 if not implemented

Implements IIterativeModel.

void Iterate ( )
inline

Run one iteration (= pass over the training data)

Implements IIterativeModel.

override void LoadModel ( string  filename)
inlinevirtual

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 
)
inlinevirtual

Predict rating or score for a given user-item combination

Parameters
user_idthe user ID
item_idthe item ID
Returns
the predicted score/rating for the given user-item combination

Implements Recommender.

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.

override void SaveModel ( string  filename)
inlinevirtual

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Reimplemented from Recommender.

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.

Implements IRecommender.

override void Train ( )
inlinevirtual

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

float global_average
protected

the global rating average

float [] item_biases
protected

the item biases

float max_rating
protectedinherited

Maximum rating value

float min_rating
protectedinherited

Minimum rating value

IRatings ratings
protectedinherited

rating data

float [] user_biases
protected

the user biases

Property Documentation

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

virtual IRatings Ratings
getsetinherited

The rating data

float RegI
getset

Regularization parameter for the item biases

float RegU
getset

Regularization parameter for the user biases


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