Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Grid Information Services for Distributed Resource Sharing
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
On search in peer-to-peer file sharing systems
Proceedings of the 2005 ACM symposium on Applied computing
Distributed text retrieval from overlapping collections
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
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Measuring effectiveness of Distributed Information Retrieval (DIR) is essential for research and development and for monitoring search quality in dynamic environment. Numerous works have been done to propose new search models in the context of peer-to-peer information retrieval systems (P2P-IR). In this article, we are considering another problem, which is the global ranking of a set of results' lists coming from a large set of IR systems. In this article we define a new method for automatic aggregation of results which mixes these categories by allowing each peer to construct knowledge about other peers' relevance model using a learning method (Formal Concept Analysis). The idea is that each peer constructs relationships between past queries, returned documents and contributed peers.