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.

中文翻译：

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基于书目的人推荐系统中学者的相关性感知

协作和社交网络对于学者来说越来越重要，但是要确定相关的协作者需要付出巨大的努力。尽管有各种网络服务针对用户之间的相似性进行了优化，但是产生新知识的学术动机要求在识别具有互补性的人时提供帮助。但是，对于学术界如何看待个人在其职业中的相关性，互补性和多样性以及如何将这些概念最佳地嵌入社会匹配系统中，几乎没有经验性的理解。本文旨在通过探索高级研究人员对相似或不同连续体中不同层次的推荐其他学者的看法，来支持多样性增强型人推荐系统的发展。为了进行研究，我们基于DBLP计算机科学书目中学者出版物的主题建模构建了一个推荐系统。对18位高级研究人员的研究包括对照实验和半结构化访谈，重点是他们对所给建议的相关性，相似性和熟悉性以及参与者与被推荐人互动的意愿的主观感知。该研究表明，尽管人们认识到需要互补性，但同质性偏见（选择相似他人的行为倾向）仍然很强。虽然实验表明，大多数相似水平与其他水平之间的感知相关性之间存在一致且显着的差异，但访谈结果表明，对人们推荐相关性的评估是复杂且多方面的。