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Cold-Start recommender system problem within a multidimensional data warehouse

Negre, Elsa and Ravat, Franck and Teste, Olivier and Tournier, Ronan Cold-Start recommender system problem within a multidimensional data warehouse. (2013) In: IEEE International Conference on Research Challenges in Information Science - RCIS 2013, 29 May 2013 - 31 May 2013 (Paris, France).

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Official URL: http://dx.doi.org/10.1109/RCIS.2013.6577714


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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6577714
HAL Id:hal-01148286
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université Paris-Dauphine (FRANCE)
Laboratory name:
Deposited On:04 May 2015 11:49

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