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Cold-start recommender system problem within a multidimensional data warehouse

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Date
2013
Link to item file
http://oatao.univ-toulouse.fr/12619/
Dewey
Informatique générale
Sujet
Recommender system
DOI
http://dx.doi.org/10.1109/RCIS.2013.6577714
Conference name
Seventh International Conference on Research Challenges in Information Science (RCIS 2013)
Conference date
05-2015
Conference city
Paris
Conference country
France
Book title
2013 IEEE Seventh International Conference on Research Challenges in Information Science (RCIS)
Publisher
IEEE
Publisher city
Piscataway, NJ
Year
2013
ISBN
978-1-4673-2914-9
URI
https://basepub.dauphine.fr/handle/123456789/15669
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Negre, Elsa
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ravat, Franck
34499 Institut de recherche en informatique de Toulouse [IRIT]
Teste, Olivier
34499 Institut de recherche en informatique de Toulouse [IRIT]
Tournier, Ronan
34499 Institut de recherche en informatique de Toulouse [IRIT]
Type
Communication / Conférence
Item number of pages
1-8
Abstract (EN)
Data warehouses store large volumes of consolidated and historized multidimensional data for analysis and exploration by decision-makers. Exploring data is an incremental OLAP (On-Line Analytical Processing) query process for searching relevant information in a dataset. In order to ease user exploration, recommender systems are used. However when facing a new system, such recommendations do not operate anymore. This is known as the cold-start problem. In this paper, we provide recommendations to the user while facing this cold-start problem in a new system. This is done by patternizing OLAP queries. Our process is composed of four steps: patternizing queries, predicting candidate operations, computing candidate recommendations and ranking these recommendations.

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