Cold-Start recommender system problem within a multidimensional data warehouse

Elsa Negre (1) , Franck Ravat (2, 3) , Olivier Teste (2, 4) , Ronan Tournier (2, 3)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision
2 IRIT-SIG - Systèmes d’Informations Généralisées
3 UT Capitole - Université Toulouse Capitole
4 UT2J - Université Toulouse - Jean Jaurès

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.

Mots clés: cold-start problem; Multidimensional data warehouse; OLAP

Domaines: Théorie de l'information [cs.IT]; Recherche d'information [cs.IR]

Elsa Negre, Franck Ravat, Olivier Teste, Ronan Tournier. Cold-Start recommender system problem within a multidimensional data warehouse. IEEE International Conference on Research Challenges in Information Science - RCIS 2013, May 2013, Paris, France. pp. 1-8, ⟨10.1109/RCIS.2013.6577714⟩. ⟨hal-01148286⟩