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

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  • Guillaume Cabanac

    (University of Toulouse)

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.

Suggested Citation

  • Guillaume Cabanac, 2011. "Accuracy of inter-researcher similarity measures based on topical and social clues," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 597-620, June.
    • Handle: RePEc:spr:scient:v:87:y:2011:i:3:d:10.1007_s11192-011-0358-1
    DOI: 10.1007/s11192-011-0358-1
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    Cited by:

    1. Guillaume Cabanac, 2012. "Shaping the landscape of research in information systems from the perspective of editorial boards: A scientometric study of 77 leading journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(5), pages 977-996, May.
    2. Sabine Loudcher & Wararat Jakawat & Edmundo Pavel Soriano Morales & Cécile Favre, 2015. "Combining OLAP and information networks for bibliographic data analysis: a survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 471-487, May.
    3. Guillaume Cabanac, 2012. "Shaping the landscape of research in information systems from the perspective of editorial boards: A scientometric study of 77 leading journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(5), pages 977-996, May.
    4. Jun-Ping Qiu & Ke Dong & Hou-Qiang Yu, 2014. "Comparative study on structure and correlation among author co-occurrence networks in bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1345-1360, November.
    5. Carla Mara Hilário & Maria Cláudia Cabrini Grácio & Daniel Martínez-Ávila & Dietmar Wolfram, 2023. "Authorship order as an indicator of similarity between article discourse and author citation identity in informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5389-5410, October.
    6. Guillaume Cabanac, 2013. "Experimenting with the partnership ability φ-index on a million computer scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 1-9, July.

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