Diversity exposure in social recommender systems: A social capital theory perspective

Chun Hua Tsai, Jukka Huhtamäki, Thomas Olsson, Peter Brusilovsky

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Meeting other scholars at conferences is often a stochastic, intuition-driven process. Social recommender systems can support identifying new collaboration partners that one might not naturally choose. However, to boost the accumulation of social capital, such systems must be designed for diversifying social connections. This paper draws from the extant theory on social capital and diversity exposure in recommendation systems to discuss the importance of social diversity exposure and presents design directions for social recommender systems for building social capital. As preliminary empirical insights, we report the results of a field study of two diversity-enhancing interfaces in an academic conference. Interestingly, we identified contradictory results between the subjective user feedback on the user interface quality and the objective analysis of clicking and viewing the recommendations. This implies that assessing the overall quality of a diversity-enhancing social recommender system requires careful design of suitable measurements.

Original languageEnglish (US)
Pages (from-to)57-64
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2020
Event7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2020 - Virtual, Online, Brazil
Duration: Sep 26 2020 → …

All Science Journal Classification (ASJC) codes

  • General Computer Science


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