@inproceedings{publications30544,
       booktitle = {IEEE International Conference on Research Challenges in Information Science - RCIS 2013},
           title = {Cold-Start recommender system problem within a multidimensional data warehouse},
          author = {Elsa Negre and Franck Ravat and Olivier Teste and Ronan Tournier},
       publisher = {IEEE},
            year = {2013},
           pages = {1--8},
        keywords = {Multidimensional data warehouse - OLAP - cold-start problem},
             url = {https://publications.ut-capitole.fr/id/eprint/30544/},
        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.}
}