Predicting query performance for fusion-based retrieval

  • Authors:

  • Gad Markovits;Anna Shtok;Oren Kurland;David Carmel

  • Affiliations:

  • Technion - Israel Institute of Technology, Haifa, Israel;Technion - Israel Institute of Technology, Haifa, Israel;Technion - Israel Institute of Technology, Haifa, Israel;IBM Research lab, Haifa, Israel

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

Estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments is the goal of query-performance prediction methods. Post-retrieval predictors analyze the result list of the most highly ranked documents. We address the prediction challenge for retrieval approaches wherein the final result list is produced by fusing document lists that were retrieved in response to a query. To that end, we present a novel fundamental prediction framework that accounts for this special characteristics of the fusion setting; i.e., the use of intermediate retrieved lists. The framework is based on integrating prediction performed upon the final result list with that performed upon the lists that were fused to create it; prediction integration is controlled based on inter-list similarities. We empirically demonstrate the merits of various predictors instantiated from the framework. A case in point, their prediction quality substantially transcends that of applying state-of-the-art predictors upon the final result list.