Impact of the Query Set on the Evaluation of Expert Finding Systems
June 28, 2018 Β· Declared Dead Β· π BIRNDL@SIGIR
"No code URL or promise found in abstract"
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Authors
Robin Brochier, Adrien Guille, Benjamin Rothan, Julien Velcin
arXiv ID
1806.10813
Category
cs.IR: Information Retrieval
Citations
11
Venue
BIRNDL@SIGIR
Last Checked
4 months ago
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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