Group-enhanced ranking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099392" target="_blank" >RIV/61989100:27240/15:86099392 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0925231214012405" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231214012405</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2014.03.079" target="_blank" >10.1016/j.neucom.2014.03.079</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Group-enhanced ranking

Popis výsledku v původním jazyce

An essential issue in document retrieval is ranking, which is used to rank documents by their relevancies to a given query. This paper presents a novel machine learning framework for ranking based on document groups. Multiple level labels represent the relevance of documents. The values of labels are used to quantify the relevance of the documents. According to a given query in the training set, the documents are divided into several groups based upon their relevance labels. The group with higher relevance labels is always ranked upon the ones with lower relevance labels. Further a preference strategy is introduced in the loss functions, which are sensitive to the group with higher relevance labels to enhance the group ranking method. Experimental results illustrate that the proposed approach is very effective, with a 14 percent improvement on TD2003 dataset evaluated by MAP. (C) 2014 Elsevier B.V.

Název v anglickém jazyce

Group-enhanced ranking

Popis výsledku anglicky

An essential issue in document retrieval is ranking, which is used to rank documents by their relevancies to a given query. This paper presents a novel machine learning framework for ranking based on document groups. Multiple level labels represent the relevance of documents. The values of labels are used to quantify the relevance of the documents. According to a given query in the training set, the documents are divided into several groups based upon their relevance labels. The group with higher relevance labels is always ranked upon the ones with lower relevance labels. Further a preference strategy is introduced in the loss functions, which are sensitive to the group with higher relevance labels to enhance the group ranking method. Experimental results illustrate that the proposed approach is very effective, with a 14 percent improvement on TD2003 dataset evaluated by MAP. (C) 2014 Elsevier B.V.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

A

Číslo periodika v rámci svazku

2015

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

99-105

Kód UT WoS článku

000346952200012

EID výsledku v databázi Scopus

2-s2.0-84912118302