Abstract
This paper discusses a Case-Based Reasoning (CBR) approach as a good way of incrementally improving an information retrieval strategy. The proposed approach, Cabri-n, achieves a synergy between CBR and information retrieval that aims to exploit users feedback for improving the retrieval short-term performances (during a single retrieval session) and the long-term performances (over the system's life time). The long-term improvement is achieved by managing a memory of sessions which exploits successes as well as failures of information retrieval. A typology defined over the set of potential information needs serves as a meta-index for the long-term memory, so allows a context-sensitive retrieval and adaptation of former sessions. Besides, we discuss some common issues of CBR and information retrieval making their combination a promising paradigm.
Keywords
- Information Retrieval
- Relevance Feedback
- Information Retrieval System
- Relevance Level
- Retrieval Strategy
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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© 1994 Springer-Verlag Berlin Heidelberg
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Smaïl, M. (1994). Case-based information retrieval. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_103
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DOI: https://doi.org/10.1007/3-540-58330-0_103
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