Intent-oriented diversity in recommender systems
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
ACMFecha de edición
2011Cita
10.1145/2009916.2010124
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 2011. 1211-1212
ISBN
978-1-4503-0757-4DOI
10.1145/2009916.2010124Financiado por
This work is supported by the Spanish Government (TIN2008- 06566-C04-02), and the Government of Madrid (S2009TIC-1542).Proyecto
Comunidad de Madrid. S2009/TIC-1542/MA2VICMRVersión del editor
http://dx.doi.org/10.1145/2009916.2010124Materias
Diversity; Diversity metrics; Profile aspects; Query intent; Recommender systems; User profiles; InformáticaNota
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, http://dx.doi.org/10.1145/2009916.2010124.Derechos
© The authorsResumen
Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.
Lista de ficheros
Google Scholar:Vargas, Saúl
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Castells Azpilicueta, Pablo
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Vallet Weadon, David Jordi
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