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 might provide.

Conclusion

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 user-system cooperation.

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.

References

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),

92–99; https://doi.org/10.1016/j.obhdp.2013.04.003.

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. org/10.1145/3025453.3025974

4. Aral, S. and Van Alstyne, M. The diversity-bandwidth trade-off. American J. Sociology 117, 1 (2011), 90–171; http://doi.org/10.1086/661238

5. Barabási, A.-L. and Albert, R. Emergence of scaling in random networks. Science 286, 5439 (1999)), 509–

512; http://doi.org/10.1126/SCIENCE.286.5439.509

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. org/10.1086/421787

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 Services, 2012.

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/ pubmed/17605587.

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. org/10.1007/978-1-4899-7637-6_ 15

16. Helberger, N., Karppinen, K. and D’Acunto, L. Exposure diversity as a design principle for recommender systems. Information, Communication & Society 21

(2018), 191–207.

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; http://dx.doi.org/10.1037/h0034747

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. knosys.2017.02.009.

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; http://doi.org/10.1145/3131782

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. org/10.1145/1125451.1125659

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:// www.ejkm.com

28. Obstfeld, D. Social networks, the tertius iungens orientation, and involvement in innovation.

Administrative Science Quarterly 50, 1 (2005), 100–130; http://asq.sagepub.com/content/50/1/100.short.

29. O’Neill, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

Crown, 2016.

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. org/10.1080/10580530801941124

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, Ltd. 541–562.

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; https://doi.org/10.1145/1096737.1096740

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 Retrieval.

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),

42–44; http://doi.org/10.1145/3132698

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. org/10.1016/j.dss.2012.12.020

41. Wu, W., Chen, L. and Zhao, Y. Personalizing recommendation diversity based on user personality.

User Modeling and User-Adapted Interaction (July

2018); https://doi.org/10.1007/s11257-018-9205-x

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; https://doi.org/10.1145/3173574.3173682

Thomas Olsson ( [e-mail]) is an associate professor at Tampere University, Finland.

Jukka Huhtamäki ( [e-mail]) is a postdoctoral researcher at Tampere University, Finland.

Hannu Kärkkäinen ( [e-mail]) is a professor of information knowledge and management at Tampere University, Finland.

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