Incremental Test Collections

Corpora and topics are readily available for information retrieval research. Relevance judgments, which are necessary for system evaluation, are expensive; the cost of obtaining them prohibits in-house evaluation of retrieval systems on new corpora or new topics. We present an algorithm for cheaply constructing sets of relevance judgments. Our method intelligently selects documents to be judged and decides when to stop in such a way that with very little work there can be a high degree of condence in the result of the evaluation. We demonstrate the algorithm\u27s eectiveness by showing that it produces small sets of relevance judgments that reliably discriminate between two systems. The algorithm can be used to incrementally design retrieval systems by simultaneously comparing sets of systems. The number of additional judgments needed after each incremental design change decreases at a rate reciprocal to the number of systems being compared. To demonstrate the eectiveness of our method, we evaluate TREC ad hoc submissions, showing that with 95% fewer relevance judgments we can reach a Kendall\u27s tau rank correlation of at least 0.9

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions