es.uam.eps.ir.ranksys.diversity.distance.metrics

Class EILD<U,I>

java.lang.Object

es.uam.eps.ir.ranksys.metrics.AbstractRecommendationMetric<U,I>

es.uam.eps.ir.ranksys.diversity.distance.metrics.EILD<U,I>

Type Parameters:

U - type of the users

I - type of the items

All Implemented Interfaces:

RecommendationMetric<U,I>

public class EILD<U,I>
extends AbstractRecommendationMetric<U,I>

Expected Intra-List Diversity metric. S. Vargas and P. Castells. Rank and relevance in novelty and diversity for Recommender Systems. RecSys 2011.

Author:

Saúl Vargas (saul.vargas@uam.es), Pablo Castells (pablo.castells@uam.es)

Constructor Summary

Constructors

Constructor and Description
EILD(int cutoff, ItemDistanceModel<I> distModel, RelevanceModel<U,I> relModel, RankingDiscountModel disc) Constructor with a single ranking discount model.
EILD(int cutoff, ItemDistanceModel<I> distModel, RelevanceModel<U,I> relModel, RankingDiscountModel disc1, RankingDiscountModel disc2) Constructor with a two ranking discount models: for global ranking and ranking gap between items.

Method Summary

Modifier and Type	Method and Description
double	evaluate(Recommendation<U,I> recommendation) Returns a score for the recommendation list.

Methods inherited from class java.lang.Object

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

Constructor Detail

EILD

public EILD(int cutoff,
            ItemDistanceModel<I> distModel,
            RelevanceModel<U,I> relModel,
            RankingDiscountModel disc)

Constructor with a single ranking discount model.

Parameters:

cutoff - maximum length of recommendation lists to evaluate

distModel - item distance model

relModel - relevance model

disc - ranking discount model

EILD

public EILD(int cutoff,
            ItemDistanceModel<I> distModel,
            RelevanceModel<U,I> relModel,
            RankingDiscountModel disc1,
            RankingDiscountModel disc2)

Constructor with a two ranking discount models: for global ranking and ranking gap between items.

Parameters:

cutoff - maximum length of recommendation lists to evaluate

distModel - item distance model

relModel - relevance model

disc1 - ranking discount model for item ranking

disc2 - ranking discount model for ranking gap

Method Detail

evaluate

public double evaluate(Recommendation<U,I> recommendation)

Returns a score for the recommendation list.

Parameters:

recommendation - recommendation list

Returns:

score of the metric to the recommendation