ItemAverage Class Reference

Uses the average rating value of an item for prediction. More...

Inheritance diagram for ItemAverage:
EntityAverage IncrementalRatingPredictor RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings and perform incremental training.
override 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
override void LoadModel (string filename)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
override 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.
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)
void Retrain (int entity_id, IList< int > indices)
 Retrain the recommender according to the given entity type.
void Train (IList< int > entity_ids, int max_entity_id)
 Train the recommender according to the given entity type.

Protected Attributes

IList< float > entity_averages
 The average rating for each entity.
float global_average
 The global average rating (default prediction if there is no data about an entity).
float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

Properties

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.
virtual IRatings Ratings [get, set]
 The rating data.
float this [int index] [get]
 return the average rating for a given entity
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

Uses the average rating value of an item for prediction.

This recommender supports incremental updates.


Member Function Documentation

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

Add new ratings and perform incremental training.

Parameters:
ratings the ratings

Reimplemented from IncrementalRatingPredictor.

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

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_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Reimplemented from RatingPredictor.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

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 RatingPredictor.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual]

Predict rating or score for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted score/rating for the given user-item combination

Implements RatingPredictor.

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

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

Reimplemented from IncrementalRatingPredictor.

override void RemoveRatings ( IDataSet  ratings  )  [inline, virtual]

Remove existing ratings and perform "incremental" training.

Parameters:
ratings the 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_id the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

void Retrain ( int  entity_id,
IList< int >  indices 
) [inline, protected, inherited]

Retrain the recommender according to the given entity type.

Parameters:
entity_id the ID of the entity to update
indices list of indices to use for retraining
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 RatingPredictor.

override string ToString (  )  [inline, inherited]
void Train ( IList< int >  entity_ids,
int  max_entity_id 
) [inline, protected, inherited]

Train the recommender according to the given entity type.

Parameters:
entity_ids list of all entity IDs in the training data (per rating)
max_entity_id the maximum entity ID
override void UpdateRatings ( IRatings  ratings  )  [inline, virtual]

Update existing ratings and perform incremental training.

Parameters:
ratings the ratings

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

IList<float> entity_averages [protected, inherited]

The average rating for each entity.

float global_average [protected, inherited]

The global average rating (default prediction if there is no data about an entity).

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

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.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.

float this[int index] [get, inherited]

return the average rating for a given entity

Parameters:
index the entity index
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:
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