Time-aware bias model with frequencies More...

Inheritance diagram for TimeAwareBaselineWithFrequencies:

Public Member Functions
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...

override float	ComputeObjective ()
	Compute the current optimization objective (usually loss plus regularization term) of the model More...

virtual void	Iterate ()
	Run one iteration (= pass over the training data) More...

virtual void	LoadModel (string file)
	Get the model parameters from a file More...

override float	Predict (int user_id, int item_id, DateTime time)
	predict rating at a certain point in time 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	SaveModel (string file)
	Save the model parameters to a file More...

	TimeAwareBaselineWithFrequencies ()
	Default constructor 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...

Protected Member Functions
override void	InitModel ()
	Initialize the model parameters More...

override float	Predict (int user_id, int item_id, int day, int bin)
	Predict the specified user_id, item_id, day and bin More...

int	RelativeDay (DateTime datetime)
	Given a DateTime object, return the day relative to the first rating day in the dataset More...

override void	UpdateParameters (int u, int i, int day, int bin, float err)
	Single stochastic gradient descent step: update the parameter values for one user and one item More...

Protected Attributes
int	latest_relative_day
	last day in the training data, counting from the first day More...

float	max_rating
	Maximum rating value More...

float	min_rating
	Minimum rating value More...

IRatings	ratings
	rating data More...

ITimedRatings	timed_ratings
	rating data, including time information More...

Properties
float	AlphaLearnRate `[get, set]`
	learn rate for the user-wise alphas More...

float	Beta `[get, set]`
	beta parameter for modeling the drift in the user bias More...

int	BinSize `[get, set]`
	bin size in days for modeling the time-dependent item bias More...

float	FrequencyLogBase `[get, set]`
	logarithmic base for the frequency counts More...

float	ItemBiasAtFrequencyLearnRate `[get, set]`
	learn rate for b_{i, f_{ui}} More...

float	ItemBiasByTimeBinLearnRate `[get, set]`
	learn rate for the bin-wise item bias More...

float	ItemBiasLearnRate `[get, set]`
	learn rate for the item bias 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 over the dataset to perform More...

override IRatings	Ratings `[get, set]`

float	RegAlpha `[get, set]`
	regularization for the user-wise alphas More...

float	RegI `[get, set]`
	regularization for the item bias More...

float	RegItemBiasAtFrequency `[get, set]`
	regularization constant for b_{i, f_{ui}} More...

float	RegItemBiasByTimeBin `[get, set]`
	regularization for the bin-wise item bias More...

float	RegU `[get, set]`
	regularization for the user bias More...

float	RegUserBiasByDay `[get, set]`
	regularization for the day-wise user bias More...

float	RegUserScaling `[get, set]`
	regularization for the user scaling factor More...

float	RegUserScalingByDay `[get, set]`
	regularization for the day-wise user scaling factor More...

virtual ITimedRatings	TimedRatings `[get, set]`
	the rating data, including time information More...

float	UserBiasByDayLearnRate `[get, set]`
	learn rate for the day-wise user bias More...

float	UserBiasLearnRate `[get, set]`
	learn rate for the user bias More...

float	UserScalingByDayLearnRate `[get, set]`
	learn rate for the day-wise user scaling factor More...

float	UserScalingLearnRate `[get, set]`
	learn rate for the user-wise scaling factor More...

Detailed Description

Time-aware bias model with frequencies

Model described in equation (11) of BellKor Grand Prize documentation for the Netflix Prize (see below).

The default hyper-parameter values are set to the ones shown in the report. For datasets other than Netflix, you may want to find better parameters.

Literature:

Yehuda Koren: The BellKor Solution to the Netflix Grand Prize

This recommender does currently NOT support incremental updates.

Constructor & Destructor Documentation

TimeAwareBaselineWithFrequencies ( )

inline

Default constructor

Member Function Documentation

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_id	the user ID
item_id	the 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

override float ComputeObjective ( )

inlinevirtual

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

Returns: the current objective; -1 if not implemented

Reimplemented from TimeAwareBaseline.

override void InitModel ( )

inlineprotectedvirtual

Initialize the model parameters

Reimplemented from TimeAwareBaseline.

virtual void Iterate ( )

inlinevirtualinherited

Run one iteration (= pass over the training data)

Implements IIterativeModel.

virtual void LoadModel ( string filename )

inlinevirtualinherited

Get the model parameters from a file

Parameters

filename the name of the file to read from

Implements IRecommender.

Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, EntityAverage, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.

override float Predict	(	int	user_id,
		int	item_id,
		int	day,
		int	bin
	)

inlineprotectedvirtual

Predict the specified user_id, item_id, day and bin

Assumes user and item IDs are valid.

Parameters

user_id	the user ID
item_id	the item ID
day	the day of the rating
bin	the day bin of the rating

Reimplemented from TimeAwareBaseline.

override float Predict	(	int	user_id,
		int	item_id,
		DateTime	time
	)

inlinevirtual

predict rating at a certain point in time

Parameters

user_id	the user ID
item_id	the item ID
time	the time of the rating event

Reimplemented from TimeAwareBaseline.

override float Predict	(	int	user_id,
		int	item_id
	)

inlinevirtualinherited

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 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_id	the user ID
n	the number of items to recommend, -1 for as many as possible
ignore_items	collection if items that should not be returned; if null, use empty collection
candidate_items	the candidate items to choose from; if null, use all items

Returns: a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

int RelativeDay ( DateTime datetime )

inlineprotectedinherited

Given a DateTime object, return the day relative to the first rating day in the dataset

Returns: the day relative to the first rating day in the dataset

Parameters

datetime the date/time of the rating event

virtual void SaveModel ( string filename )

inlinevirtualinherited

Save the model parameters to a file

Parameters

filename the name of the file to write to

Implements IRecommender.

Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, MostPopular, KNN, SLIM, MostPopularByAttributes, EntityAverage, ExternalItemRecommender, ExternalRatingPredictor, GlobalAverage, Constant, Random, Random, and Zero.

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

Reimplemented from TimeAwareBaseline.

override void UpdateParameters	(	int	u,
		int	i,
		int	day,
		int	bin,
		float	err
	)

inlineprotectedvirtual

Single stochastic gradient descent step: update the parameter values for one user and one item

Parameters

u	the user ID
i	the item ID
day	the day of the rating
bin	the day bin of the rating
err	the current error made for this rating

Reimplemented from TimeAwareBaseline.

Member Data Documentation

int latest_relative_day

protectedinherited

last day in the training data, counting from the first day

float max_rating

protectedinherited

Maximum rating value

float min_rating

protectedinherited

Minimum rating value

IRatings ratings

protectedinherited

rating data

ITimedRatings timed_ratings

protectedinherited

rating data, including time information

Property Documentation

float AlphaLearnRate

getsetinherited

learn rate for the user-wise alphas

float Beta

getsetinherited

beta parameter for modeling the drift in the user bias

int BinSize

getsetinherited

bin size in days for modeling the time-dependent item bias

float FrequencyLogBase

getset

logarithmic base for the frequency counts

float ItemBiasAtFrequencyLearnRate

getset

learn rate for b_{i, f_{ui}}

float ItemBiasByTimeBinLearnRate

getsetinherited

learn rate for the bin-wise item bias

float ItemBiasLearnRate

getsetinherited

learn rate for the item bias

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 over the dataset to perform

float RegAlpha

getsetinherited

regularization for the user-wise alphas

float RegI

getsetinherited

regularization for the item bias

float RegItemBiasAtFrequency

getset

regularization constant for b_{i, f_{ui}}

float RegItemBiasByTimeBin

getsetinherited

regularization for the bin-wise item bias

float RegU

getsetinherited

regularization for the user bias

float RegUserBiasByDay

getsetinherited

regularization for the day-wise user bias

float RegUserScaling

getsetinherited

regularization for the user scaling factor

float RegUserScalingByDay

getsetinherited

regularization for the day-wise user scaling factor

virtual ITimedRatings TimedRatings

getsetinherited

the rating data, including time information

float UserBiasByDayLearnRate

getsetinherited

learn rate for the day-wise user bias

float UserBiasLearnRate

getsetinherited

learn rate for the user bias

float UserScalingByDayLearnRate

getsetinherited

learn rate for the day-wise user scaling factor

float UserScalingLearnRate

getsetinherited

learn rate for the user-wise scaling factor

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

TimeAwareBaselineWithFrequencies.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation