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Diversity Exposure in Social Recommender Systems: A Social Capital Theory Perspective

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Presentation at the IntRS at RecSys 2020. In collaboration with Chun-Hua Tsai, Thomas Olsson & Peter Brusilovsky.

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Diversity Exposure in Social Recommender Systems: A Social Capital Theory Perspective

  1. 1. Diversity Exposure in Social Recommender Systems: A Social Capital Theory Perspective Chun-Hua Tsai, Jukka Huhtamäki (@jnkka), Thomas Olsson & Peter Brusilovsky IntRS 2020 at RecSys2020 http://ceur-ws.org/Vol-2682/paper6.pdf
  2. 2. How to recommend people in conferences? •Existing social structure, or the lack of one, tends to direct the emergence of new ties in conferences •For junior scholars and other newcomers, the identification of relevant individuals or cliques is laborious and characterized by chance •For seniors, a key issue, perhaps counter-intuitively, is the existence of their strong connecting tissue to the core of the community that limits their networking capability 26.9.2020 | 2
  3. 3. Key concepts •Social capital: bonding and bridging (Putnam 2000, Granovetter 1973) •Strong and weak ties (Granovetter, 1973) •Social diversity exposure (see Helberger, Karppinen & D’Acunto, 2018) 26.9.2020 | 3 Image: Olsson, Huhtamäki & Kärkkäinen (2020)
  4. 4. Social capital in knowledge work 26.9.2020 | 4 • Social capital is a key driver of organizational advantage: “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (Nahapiet & Ghoshal, 1998) • Two types of social capital, bridging and bonding. Bonding consists of strong ties, bridging capital of weak ties (Putnam 2000, Granovetter 1973) • Brokerage in a social network is a favorable position to an actor (Burt, 2004)
  5. 5. Social capital accumulation • Social capital is dynamic: “The existence of a network of connections is not a natural given, or even a social given, constituted once and for all by an initial act of institution [...] It is the product of an endless effort at institution.” (Bourdieu, 1986) • Network evolution is prone to biases: homophily (Kossinets & Watts, 2009), triadic closure (Granovetter, 1973), and in some cases preferential attachment (Barabási & Albert, 1999) • Social capital accumulates one network connection at a time. Here, we explore how to support the selection of these connections with a people recommender system 26.9.2020 | 5
  6. 6. Relevance-first vs. diversity exposure •Key mechanisms driving the evolution of a social network are homophily and triadic closure •A relevance-first approach to recommend new people is likely to amplify these mechanism, leading into tightly connected social communities •Instead, to counter-balance the natural evolution of social networks, we suggest to focus on social diversity exposure and the identification of weak ties for bridging social capital 26.9.2020 | 6
  7. 7. Illustrative case study 26.9.2020 | 7
  8. 8. First insights 26.9.2020 | 8 • 170 participants at EC-TEL 2017 • Conference Navigator (CN3) (Tsai & Brusilovsky, 2016)
  9. 9. Reflections • Capturing real-life social works: the proceedings publication certainly gives a very limited view • Alternative to social diversity exposure: transparency of algorithms and interfaces • Yet, people are prone to homophily and triadic closure • Are we able to able to algorithmically argue for the importance of social diversity exposure and the accumulation of bridging social capital? • The ethics of nudging: technology is never neutral • Finally, do we need (social) theory in RecSys research? 26.9.2020 | 9
  10. 10. Additional reading • Digital Social Matching in Professional Life: Lessons from the Big Match project • Huhtamäki, J., Olsson, T., & Laaksonen, S.-M. (2020). Facilitating Organisational Fluidity with Computational Social Matching. In H. Lehtimäki, P. Uusikylä, & A. Smedlund (Eds.), Society as an Interaction Space: A Systemic Approach (pp. 229–245). Springer. • Olshannikova, E., Olsson, T., Huhtamäki, J., & Yao, P. (2019). Scholars’ Perceptions of Relevance in Bibliography-based People Recommender System. Computer Supported Cooperative Work (CSCW), 28(3–4), 357–389. • Olshannikova, E., Olsson, T., Huhtamäki, J., Paasovaara, S., & Kärkkäinen, H. (2020). From Chance to Serendipity: Knowledge Workers’ Experiences of Serendipitous Social Encounters. Advances in Human-Computer Interaction, 2020, 18. • Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2020). Directions for Professional Social Matching Systems. Communications of the ACM, 63(2), 60–69. • Skenderi, E., Olshannikova, E., Olsson, T., Huhtamäki, J., Koivunen, S., Yao, P., & Huttunen, H. (2019). Investigation of Egocentric Social Structures for Diversity-Enhancing Followee Recommendations. Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization - UMAP’19 Adjunct, 257–261. 26.9.2020 | 10@jnkka

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