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Scholars’ Perceptions of Relevance in Bibliography-Based People Recommender System

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
JulkaisuComputer Supported Cooperative Work: CSCW: An International Journal
DOI - pysyväislinkit
TilaE-pub ahead of print - 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

Collaboration and social networking are increasingly important for academics, yet identifying relevant collaborators requires remarkable effort. While there are various networking services optimized for seeking similarities between the users, the scholarly motive of producing new knowledge calls for assistance in identifying people with complementary qualities. However, there is little empirical understanding of how academics perceive relevance, complementarity, and diversity of individuals in their profession and how these concepts can be optimally embedded in social matching systems. This paper aims to support the development of diversity-enhancing people recommender systems by exploring senior researchers’ perceptions of recommended other scholars at different levels on a similar–different continuum. To conduct the study, we built a recommender system based on topic modeling of scholars’ publications in the DBLP computer science bibliography. A study of 18 senior researchers comprised a controlled experiment and semi-structured interviewing, focusing on their subjective perceptions regarding relevance, similarity, and familiarity of the given recommendations, as well as participants’ readiness to interact with the recommended people. The study implies that the homophily bias (behavioral tendency to select similar others) is strong despite the recognized need for complementarity. While the experiment indicated consistent and significant differences between the perceived relevance of most similar vs. other levels, the interview results imply that the evaluation of the relevance of people recommendations is complex and multifaceted. Despite the inherent bias in selection, the participants could identify highly interesting collaboration opportunities on all levels of similarity.

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