Cold-Start recommender system problem within a multidimensional data warehouse - Archive ouverte HAL Access content directly
Conference Papers Year : 2013

Cold-Start recommender system problem within a multidimensional data warehouse

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.
Fichier principal
Vignette du fichier
Negre_12619.pdf (430.81 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01148286 , version 1 (04-05-2015)

Identifiers

Cite

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⟩
201 View
112 Download

Altmetric

Share

Gmail Facebook X LinkedIn More