Improving novelty in streaming recommendation using a context model
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Gediminas Adomavicius; Linas Baltrunas; Ernesto William de Luca; Tim Hussein; Alexander TuzhilinDate
2012Citation
CARS-2012: Proceedings of the 4th Workshop on Context-Aware Recommender Systems. Ed. Gediminas Adomavicius, Linas Baltrunas, Ernesto William de Luca, Tim Hussein, Alexander Tuzhilin. CEUR Workshop Proceedings, Volumen 889, 2012ISSN
1611-3349Funded by
This work was supported by the Ministerio de Educaci on y Ciencia under the grant N. TIN2011-28538-C02, Novelty, di- versity, context and time: newdimensions in next-generation information retrieval and recommender systems.Editor's Version
http://ceur-ws.org/Vol-889/Subjects
Novelty; Diversity; User Model; Context; InformáticaNote
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Rights
© the author(s)Abstract
In recent years there has been an increasing research interest
in novelty/diversity detection in Information Retrieval and
in Recommendation Systems. We propose a model that increases
the novelty of recommendations using a context user
profile that was created automatically using self-organizing
maps. Our system was evaluated on the Reuters Corpus
Volume 1 and our experiments show that filtering the recommended
items using a novelty score derived from the contextbased
user profile provides better search results in terms of
novel information that is presented to the user.
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Google Scholar:Dumitrescu, Doina Alexandra
-
Santini, Simone
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