Persuasion can also manifest itself
via facilitation of forming the connection. For example, in an event context
with low-risk matching, the matched
actors can be given a playful challenge to explore the commonalities
and complementariness by not revealing the exact reason behind the
recommendation, such as in Chen et
al. 8 Here, the human capabilities in
identifying possible matches could
also be harnessed beyond the primary
end user, for example, encouraging a
third party to serve as a matchmaker
between people they know, thus facilitating newly recommended connections and validating the ill-reasoned
recommendations that an algorithm
Social matching is ubiquitous in professional life, yet suboptimally supported by computational systems.
Based on a multidisciplinary review of
the literature, we outline the complex
problem space of Professional Social
Matching, and we define design goals
for computer-supported PSM and requirements for developing next-generation systems.
We suggest that many conventional
approaches in recommender systems
and social link prediction, when applied to PSM, could have detrimental
long-term implications for organizations’ and individuals’ performance.
Conventional mechanisms, such as
optimizing for similarity and triadic
closure, involve risks of strengthening
the human biases of homophily and
echo chambering. We call for diversity-enhancing and contextually sensitive
designs of future PSM systems, tapping on multi-dimensional analytics
of not only the potential matched actors but also the intended type of collaboration and organizational context.
Further, we call for a paradigmatic
shift from automated recommender
systems toward decision-support systems that are based on meaningful
All in all, analysis of this application
area underlines the importance and urgency of rethinking some paradigmatic
algorithmic approaches. In such a complex and interdisciplinary area as PSM,
reconsidering the conventions is not
only beneficial; it is a necessity.
1. Adomavicius, G. and Tuzhilin, A. Context-aware
recommender systems. Recommender Systems
Handbook. F. Ricci, L. Rokach, and B. Shapira, eds.
Springer, 2011, Boston, MA, 217¬253.
2. Aggar wal, I. and Woolley, A. W. Do you see what I
see? The effect of members’ cognitive styles on team
processes and errors in task execution. Organizational
Behavior and Human Decision Processes 122, 1 (2013),
3. Alkhatib, A., Bernstein, M.S. and Levi, M. Examining
crowd work and gig work through the historical
lens of piecework. In Proceedings of the 2017
CHI Conf. Human Factors in Computing Systems,
4599–4616. ACM Press, New York, NY; http://doi.
4. Aral, S. and Van Alstyne, M. The diversity-bandwidth
trade-off. American J. Sociology 117, 1 (2011), 90–171;
5. Barabási, A.-L. and Albert, R. Emergence of scaling in
random networks. Science 286, 5439 (1999)), 509–
6. Bendoly, E. Fit, bias and enacted sensemaking in data
visualization: Frameworks for continuous development
in operations and supply chain management analytics.
J. Business Logistics 37, 1 (2016), 6–17.
7. Burt, R.S. Structural holes and good ideas. American
J. Sociology 110, 2 (2004), 349–399; http://doi.
8. Chen, J. and Abouzied, A. One LED is enough:
Catalyzing face-to-face interactions at conferences
with a gentle nudge. In Proceedings of the 19th ACM
Conf. Computer-Supported Cooperative Work & Social
Computing, 2016, 172–183.
9. Davenport, T.H. Analytics 3.0. Harvard Business
Review (Dec. 2013).
10. Easley, D. and Kleinberg, J. Networks, Crowds, and
Markets: Reasoning About a Highly Connected World.
Cambridge University Press, 2010.
11. Faliagka, E., Ramantas, K., Tsakalidis, A. and Tzimas,
G. Application of machine learning algorithms to an
online recruitment system. In Proceedings of the
7th Intern. Conf. Internet and Web Applications and
12. Fogg, B. J. Persuasive technology: Using computers to
change what we think and do. Ubiquity (Dec. 2002).
13. Gonzaga, G.C., Campos B., Bradbury T. Similarity,
convergence, and relationship satisfaction in dating
and married couples. J. Pers Soc Psychol. 93, 1
(July 2007), 34–48; https://www.ncbi.nlm.nih.gov/
14. Granovetter, M. The strength of weak ties. American J.
Sociology 78, 6 (May 1973), 1360–1380.
15. Guy, I. Social recommender systems. Recommender
Systems Handbook. F. Ricci, L. Rokach, and B.
Shapira, eds. Springer, 2011, 511–543; https://doi.
16. Helberger, N., Karppinen, K. and D’Acunto, L. Exposure
diversity as a design principle for recommender
systems. Information, Communication & Society 21
17. Huhtamäki, J., Russell, M.G., Rubens, N. and Still,
K. Ostinato: The exploration-automation cycle of
user-centric, process-automated data-driven visual
network analytics. Transparecy in Social Media,
Springer, 2015, 197–222; http://bit.ly/2lGmvyM
18. Hupa, K.R., Wierzbicki, A. and Datta, A.
Interdisciplinary matchmaking: Choosing
collaborators by skill, acquaintance and trust. Comput.
Commun. Netw. (2010), 319–347.
19. Kahneman, D. and Tversky, A. On the psychology of
prediction. Psychological Review 80, 4 (1973), 237-251;
20. Koren Y. and Bell R. Advances in collaborative filtering.
Recommender Systems Handbook. F. Ricci, L. Rokach,
and B. Shapira, eds. Springer, 2011, Boston, MA.
21. Kossinets, G. and Watts, D.J. Origins of homophily in an
evolving social network. American J. Sociology 115, 2
(2009), 405–450; http://doi.org/10.1086/599247
22. Kotkov, D., Wang, S. and Veijalainen, J. A survey of
serendipity in recommender systems.
Knowledge-Based Systems 111 (2016), 180–192.
23. Kunaver, M. and Požrl, T. Diversity in recommender
systems—A survey. Knowledge-Based Systems
123 (2017), 154–162; https://doi.org/10.1016/j.
24. Li, Z., Fang, X. and Sheng, O.R. L. A survey of link
recommendation for social networks. ACM Trans.
Management Information Systems 9, 1 (2017), 1–26;
25. McCay-Peet, L. and Toms, E.G. The process of
serendipity in knowledge work. In Proceedings of the
3rd Symp. Information Interaction in Context. ACM,
New York, NY, 2010, 377–382.
26. McNee, S.M., Riedl, J. and Konstan, J.A. Being
accurate is not enough. In Proceedings of CHI 2006
Extended Abstracts on Human Factors in Computing
Systems. ACM Press. New York, NY; http://doi.
27. Mitchell, R. and Nicholas, S. Knowledge Creation in
Groups: The Value of Cognitive Diversity, Transactive
Memory, and Open-mindedness Norms. The Electronic
J. Knowledge Management 4, 1 (2006), 67–74; https://
28. Obstfeld, D. Social networks, the tertius iungens
orientation, and involvement in innovation.
Administrative Science Quarterly 50, 1 (2005), 100–130;
29. O’Neill, C. Weapons of Math Destruction: How Big
Data Increases Inequality and Threatens Democracy.
30. Pham, M. C., Kovachev, D., Cao, Y., Mbogos, G. M. and
Klamma, R. Enhancing academic event participation
with context-aware and social recommendations. In
Proceedings of the 2012 IEEE/ACM Intern. Conf. Advances
in Social Networks Analysis and Mining, 464–471.
31. Power, D. J. Understanding data-driven decision
support systems. Information Systems
Management 25, 2 (2008), 149–154. http://doi.
32. Rodan, S. and Galunic, C. More than network
structure: how knowledge heterogeneity influences
managerial performance and innovativeness. Strategic
Management J. 25, 6 (June 2004). John Wiley & Sons,
33. Shilton, Katie Values and Ethics in Human-Computer
Interaction. Foundations and Trends in Human-Computer Interaction 12, 2 (2018), 107–171.
34. Simon, H.A. Models of Man. New York, Wiley & Sons, 1957.
35. Tanghe, J., Wisse, B. and van der Flier, H. The
formation of group affect and team effectiveness: The
moderating role of identification. Br. J. Manag. 21, 2
(July 2010), 340–358.
36. Terveen, L. and McDonald, D. W. Social matching:
A framework and research agenda. ACM Trans.
Comput.-Hum. Interact. 12, 3 (Sept. 2005), 401–434;
37. Tsai, C.-H. and Brusilovsky, P. Beyond the ranked
list: User-driven exploration and diversification of
social recommendation. In Proceedings of the 2018
Conference on Human Information Interaction and
38. Verbert, K., Parra, D. and Brusilovsky, P. Agents
vs. users: Visual recommendation of research
talks with multiple dimension of relevance. ACM
Trans. Interactive Intelligent Systems 6, 2 (2016),
11:1–11: 42; http://doi.org/10.1145/2946794
39. Wallach, H. Computational social science ≠ computer
science + social data. Comm. ACM 61, 3 (Mar. 2018),
40. Wang, G.A., Jiao, J., Abrahams, A.S., Fan, W. and
Zhang, Z. ExpertRank: A topic-aware expert finding
algorithm for online knowledge communities. Decision
Support Systems 54, 3 (2013).), 1442–1451; http://doi.
41. Wu, W., Chen, L. and Zhao, Y. Personalizing
recommendation diversity based on user personality.
User Modeling and User-Adapted Interaction (July
42. Zhou, S., Valentine, M. and Bernstein, M.S. In search of
the dream team: Temporally constrained multi-armed
bandits for identifying effective team structures. In
Proceedings of the 2018 CHI Conference on Human
Factors in Computing Systems. ACM, New York, NY;
Thomas Olsson ( email@example.com) is an associate
professor at Tampere University, Finland.
Jukka Huhtamäki ( firstname.lastname@example.org) is a
postdoctoral researcher at Tampere University, Finland.
Hannu Kärkkäinen ( email@example.com) is a
professor of information knowledge and management at
Tampere University, Finland.
© 2020 ACM 0001-0782/20/2