Cold-Start recommender system problem within a multidimensional data warehouse

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

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Abstract

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)
Language: English
Date: 2013
Uncontrolled Keywords: Multidimensional data warehouse - OLAP - cold-start problem
Subjects: H- INFORMATIQUE
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 27 Mar 2019 14:48
Last Modified: 27 Mar 2019 14:48
URI: http://publications.ut-capitole.fr/id/eprint/30544

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