Bibtex de la publication

@Article{ Ca2011.11,
author = {Cabanac, Guillaume},
title = "{Accuracy of inter-researcher similarity measures based on topical and social clues}",
journal = {Scientometrics},
publisher = {Springer},
address = {},
year = {2011},
month = {juin},
volume = {87},
number = {3},
pages = {597--620},
language = {anglais},
URL = {,%20},
keywords = {Similarity among Researchers, Topical Clues, Social Clues, Literature Review, Recommendation, Experiment, Human Perception, Measurement},
note = {SIGRI IF:2.793},
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.}