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.
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Adomavicius, G., Tuzhilin, A. (2001). Multidimensional Recommender Systems: A Data Warehousing Approach. In: Fiege, L., Mühl, G., Wilhelm, U. (eds) Electronic Commerce. WELCOM 2001. Lecture Notes in Computer Science, vol 2232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45598-1_17
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DOI: https://doi.org/10.1007/3-540-45598-1_17
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