Query Routing is a critical step in P2P Information Retrieval. In thispaper, we consider learning to rank approaches for query routing in the clusteredP2P IR architecture. Our formulation, LTRo, scores resources based on the numberof relevant documents for each training query, and uses that information tobuild a model that would then rank promising peers for a new query. Our empiricalanalysis over a variety of P2P IR testbeds illustrate the superiority of ourmethod against the state-of-the-art methods for query routing.
|Title of host publication||LTRo: Learning to Route Queries in Clustered P2P IR|
|Publication status||Published - 08 Apr 2017|
|Event||ECIR 2017 - Aberdeen, Aberdeen, United Kingdom|
Duration: 08 Apr 2017 → 13 Apr 2017
|Name||Lecture Notes in Computer Science|
|Period||08/04/2017 → 13/04/2017|
Alkhawaldeh, R. S., Padmanabhan, D., Jose, J. M., & Yuan, F. (2017). LTRo: Learning to Route Queries in Clustered P2P IR. In LTRo: Learning to Route Queries in Clustered P2P IR (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-56608-5_42