es.upm.etsisi.cf4j.recommender.knn

Class UserKNN

java.lang.Object

es.upm.etsisi.cf4j.recommender.Recommender

es.upm.etsisi.cf4j.recommender.knn.UserKNN

public class UserKNN
extends Recommender

Implements user-to-user KNN based collaborative filtering

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	UserKNN.AggregationApproach Available aggregation approaches to merge k-nearest neighbors ratings

Field Summary

Fields

Modifier and Type	Field and Description
protected UserKNN.AggregationApproach	aggregationApproach Aggregation approach used to aggregate k-nearest neighbors ratings
protected UserSimilarityMetric	metric Similarity metric to compute the similarity between two users
protected int[][]	neighbors Contains the neighbors indexes of each user
protected int	numberOfNeighbors Number of neighbors (k)

Fields inherited from class es.upm.etsisi.cf4j.recommender.Recommender

datamodel

Constructor Summary

Constructors

Constructor and Description
UserKNN(DataModel datamodel, int numberOfNeighbors, UserSimilarityMetric metric, UserKNN.AggregationApproach aggregationApproach) Recommender constructor
UserKNN(DataModel datamodel, Map<String,Object> params) Recommender constructor from a Map containing the recommender's hyper-parameters values.

Method Summary

Modifier and Type	Method and Description
void	fit() Estimates model parameters given the hyper-parameters
double	predict(int userIndex, int itemIndex) Computes a rating prediction
String	toString()

Methods inherited from class es.upm.etsisi.cf4j.recommender.Recommender

getDataModel, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

numberOfNeighbors

protected int numberOfNeighbors

Number of neighbors (k)

metric

protected UserSimilarityMetric metric

Similarity metric to compute the similarity between two users

aggregationApproach

protected UserKNN.AggregationApproach aggregationApproach

Aggregation approach used to aggregate k-nearest neighbors ratings

neighbors

protected int[][] neighbors

Contains the neighbors indexes of each user

Constructor Detail

UserKNN

public UserKNN(DataModel datamodel,
               Map<String,Object> params)

Recommender constructor from a Map containing the recommender's hyper-parameters values. Map object must contains the following keys:

• numberOfNeighbors: int value with the number of neighbors.
• metric:: UserSimilarityMetric instance with the similarity metric to compute the similarity between two users.
• aggregationApproach: UserKNN.AggregationApproach instance with the aggregation approach used to aggregate k-nearest neighbors ratings.

Parameters:

datamodel - DataModel instance

params - Recommender's hyper-parameters values

UserKNN

public UserKNN(DataModel datamodel,
               int numberOfNeighbors,
               UserSimilarityMetric metric,
               UserKNN.AggregationApproach aggregationApproach)

Recommender constructor

Parameters:

datamodel - DataModel instance

numberOfNeighbors - Number of neighbors (k)

metric - Similarity metric to compute the similarity between two users

aggregationApproach - Aggregation approach used to aggregate k-nearest neighbors ratings

Method Detail

fit

public void fit()

Description copied from class: Recommender

Estimates model parameters given the hyper-parameters

Specified by:

fit in class Recommender

predict

public double predict(int userIndex,
                      int itemIndex)

Description copied from class: Recommender

Computes a rating prediction

Specified by:

predict in class Recommender

Parameters:

userIndex - Index of the user in the array of Users of the DataModel instance

itemIndex - Index of the item in the array of Items of the DataModel instance

Returns:

Prediction

toString

public String toString()

Overrides:

toString in class Object