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

Autor/in	Cabanac, Guillaume
Titel	Accuracy of inter-researcher similarity measures based on topical and social clues.
Quelle	In: Scientometrics, (2011) 3, S.597-620 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-011-0358-1
Schlagwörter	Similarity among researchers; Topical clues; Social clues; Literature review; Recommendation; Experiment; Human perception; Measurement
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.
Erfasst von	OLC
Update	2023/2/05