Applying the Broadway Recommendation Computation Approach for Implementing a Query Reenement Service in the Cbkb Meta Search Engine

This work reports on the applicability of the Broadway recommendation computation approach for implementing a query reenement (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 satissed 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 reenement recommender in the context CBKB, a meta-search engine developped at XRCE.