Accuracy of inter-researcher similarity measures based on topical and social clues

Guillaume Cabanac ()
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Guillaume Cabanac: University of Toulouse

Scientometrics, 2011, vol. 87, issue 3, No 11, 597-620

Abstract: Abstract Scientific literature recommender systems (SLRSs) provide papers to researchers according to their scientific interests. Systems rely on inter-researcher similarity measures that are usually computed according to publication contents (i.e., by extracting paper topics and citations). We highlight two major issues related to this design. The required full-text access and processing are expensive and hardly feasible. Moreover, clues about meetings, encounters, and informal exchanges between researchers (which are related to a social dimension) were not exploited to date. In order to tackle these issues, we propose an original SLRS based on a threefold contribution. First, we argue the case for defining inter-researcher similarity measures building on publicly available metadata. Second, we define topical and social measures that we combine together to issue socio-topical recommendations. Third, we conduct an evaluation with 71 volunteer researchers to check researchers’ perception against socio-topical similarities. Experimental results show a significant 11.21% accuracy improvement of socio-topical recommendations compared to baseline topical recommendations.

Keywords: Similarity among researchers; Topical clues; Social clues; Literature review; Recommendation; Experiment; Human perception; Measurement (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11192-011-0358-1

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