Ranking of evolving stories through meta-aggregation

Détails

ID Serval
serval:BIB_92BD61FC164F
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
Ranking of evolving stories through meta-aggregation
Titre de la conférence
Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10
Auteur⸱e⸱s
Gordevicius J., Estrada F.J., Lee H.C., Andritsos P., Gamper J.
Editeur
ACM Press
Adresse
Toronto, Canada
ISBN
9781450300995
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Série
Conference on Information and Knowledge Management (CIKM)
Pages
1909-1912
Langue
anglais
Résumé
In this paper we focus on the problem of ranking news stories within their historical context by exploiting their content similarity. We observe that news stories evolve and thus have to be ranked in a time and query dependent manner. We do this in two steps. First, the mining step discovers metastories, which constitute meaningful groups of similar stories that occur at arbitrary points in time. Second, the ranking step uses well known measures of content similarity to construct implicit links among all metastories, and uses them to rank those metastories that overlap the time interval provided in a user query. We use real data from conventional and social media sources (weblogs) to study the impact of different meta-aggregation techniques and similarity measures in the final ranking. We evaluate the framework using both objective and subjective criteria, and discuss the selection of clustering method and similarity measure that lead to the best ranking results.
Création de la notice
22/08/2017 16:16
Dernière modification de la notice
20/08/2019 15:55
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