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Applying the Broadway Recommendation Approach to Query Refinement in the CBKB MetaSearch Engine

Jean-Marc Andreoli, Rushed Kanawati, Brigitte Trousse
This work reports on the applicability of the Broadway recommendation computation approach for implementing a query refinement (QR) service in the context of a distributed information retrieval and gathering system. The Broadway recommendation approach is based on the following hypothesis: recommend to one user items or actions that have satisfied users that have behaved similarly to her/him. The case-based reasoning (CBR) paradigm is proposed to design Broadway-based recommenders. The design of such recommenders is facilitated by the use of the indexing scheme with time-extended situation of CBR*Tools, a CBR framework developped at INRIA; a crucial feature for enabling modeling and comparing users behaviors. We show how to integrate a query refinement recommender in the context CBKB, a meta-search engine developped at XRCE.
Proc. of RAPC'99, Palaiseau, France, 15-17 June 1999

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