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Multidimensional Recommender Systems: A Data Warehousing Approach

  • Gediminas Adomavicius
  • Alexander Tuzhilin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2232)

Abstract

In this paper, we present a new data-warehousing-based approach to recommender systems. In particular, we propose to extend traditional two-dimensional user/item recommender systems to support multiple dimensions, as well as comprehensive profiling and hierarchical aggregation (OLAP) capabilities. We also introduce a new recommendation query language RQL that can express complex recommendations taking into account the proposed extensions. We describe how these extensions are integrated into a framework that facilitates more flexible and comprehensive user interactions with recommender systems.

Keywords

Recommender System Query Language Dimension List Action Movie Recommend Movie 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Gediminas Adomavicius
    • 1
  • Alexander Tuzhilin
    • 2
  1. 1.Computer Science DepartmentNew York University, Courant Institute of Mathematical SciencesNew YorkUSA
  2. 2.Information Systems DepartmentNew York University, Stern School of BusinessNew YorkUSA

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