This paper studies how to enable an effective ranked retrieval over data with categorical attributes, in particular, by supporting personalized ranked retrieval of highly relevant data. While ranked retrieval has been actively studied lately, existing efforts have focused only on supporting ranking over numerical or text data. However, many real-life data contain a large amount of categorical attributes, in combination with numerical and text attributes, which cannot be efficiently supported - unlike numerical attributes where a natural ordering is inherent, the existence of categorical attributes with no such ordering complicates both the formulation and processing of ranking. This paper studies the efficient and effective support of ranking over categorical data, as well as uniform support with other types of attributes.
Bibliographical noteFunding Information:
This work was supported by the Korean Research Foundation Grant funded by the Korean Government (MOEHRD), Basic Promotion Fund; KRF-2007-331-D00377.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence