Impact of the Query Set on the Evaluation of Expert Finding Systems

Abstract : Expertise is a loosely defined concept that is hard to formalize. Much research has focused on designing efficient algorithms for expert finding in large databases in various application domains. The evaluation of such recommender systems lies most of the time on human-annotated sets of experts associated with topics. The protocol of evaluation consists in using the namings or short descriptions of these topics as raw queries in order to rank the available set of candidates. Several measures taken from the field of information retrieval are then applied to rate the rankings of candidates against the ground truth set of experts. In this paper, we apply this topic-query evaluation methodology with the AMiner data and explore a new document-query methodology to evaluate experts retrieval from a set of queries sampled directly from the experts documents. Specifically, we describe two datasets extracted from AMiner, three baseline algorithms from the literature based on several document representations and provide experiment results to show that using a wide range of more realistic queries provides different evaluation results to the usual topic-queries.
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Communication dans un congrès
BIRNDL 2018 (SIGIR 2018), Jul 2018, Ann Arbor, Michigan, USA, France
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Dernière modification le : mercredi 31 octobre 2018 - 12:24:08
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  • HAL Id : hal-01824400, version 1
  • ARXIV : 1806.10813

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Robin Brochier, Adrien Guille, Benjamin Rothan, Julien Velcin. Impact of the Query Set on the Evaluation of Expert Finding Systems. BIRNDL 2018 (SIGIR 2018), Jul 2018, Ann Arbor, Michigan, USA, France. 〈hal-01824400〉

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