Accuracy of inter-researcher similarity measures based on topical and social clues |
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Authors: | Guillaume Cabanac |
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Affiliation: | (1) Computer Science Department, IRIT UMR 5505 CNRS, University of Toulouse, 118 route de Narbonne, 31062 Toulouse Cedex 9, France |
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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. |
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Keywords: | |
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