es.uam.eps.ir.ranksys.novdiv.itemnovelty.metrics

Class ItemNoveltyMetric<U,I>

java.lang.Object

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

es.uam.eps.ir.ranksys.novdiv.itemnovelty.metrics.ItemNoveltyMetric<U,I>

Type Parameters:

U - type of the users

I - type of the items

All Implemented Interfaces:

RecommendationMetric<U,I>

Direct Known Subclasses:

EFD, EISDC, EPC, EPD, ETD

public abstract class ItemNoveltyMetric<U,I>
extends AbstractRecommendationMetric<U,I>

Item novelty metric. S. Vargas. Novelty and diversity evaluation and enhancement in Recommender Systems. PhD Thesis. 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)

Field Summary

Fields

Modifier and Type	Field and Description
protected ItemNovelty<U,I>	novelty item novelty model

Constructor Summary

Constructors

Constructor and Description
ItemNoveltyMetric(int cutoff, ItemNovelty<U,I> novelty, RelevanceModel<U,I> relevanceModel, RankingDiscountModel disc) Constructor.

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

Field Detail

novelty

protected final ItemNovelty<U,I> novelty

item novelty model

Constructor Detail

ItemNoveltyMetric

public ItemNoveltyMetric(int cutoff,
                         ItemNovelty<U,I> novelty,
                         RelevanceModel<U,I> relevanceModel,
                         RankingDiscountModel disc)

Constructor.

Parameters:

cutoff - maximum size of the recommendation list that is evaluated

novelty - novelty model

relevanceModel - relevance model

disc - ranking discount model

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