review articles

SUPPORTING HUMAN COLLABORATION has been a central driver of the development of information and communication technology. A relatively recent approach to this end is social matching, referring to computational ways of identifying and facilitating new social connections between people. 36 Social matching is most often connected with partnering for leisurely and romantic relationships—in fact, the most well-known social matching systems revolve around dating scenarios (for example, Tindera) or triggering opportunistic interactions with strangers (for example, Happnb).

a https://tinder.com/
b https://www.happn.com/en/
This article focuses on Professional
Social Matching (PSM), which we de-
fine as the matching of individuals or
groups for vocational collaboration
and co-creation of value. This covers
organizational activities, including re-
cruitment, headhunting, community
building, and team formation within
or across organizations as well as in-
dividually driven activities like men-
toring, seeking advisory relationships,
and general networking.

From a technological perspective, computer-supported PSM is based on computational approaches to profiling actors (organizations and individuals), modeling their qualities, analyzing their mutual social suitability and relevance, and presenting the recommendations to the users. For example, prescriptive data analytics9 can utilize social network analysis (SNA) for explicating the social ties between actors and machine learning-based approaches to analyze their competences and interests and to identify suitable pairs of actors. The resulting computational system can manifest as proactive

Directions for
Professional
Social
Matching
Systems
DOI: 10.1145/3363825

Future PSM systems will require diversity-enhancing yet contextually sensitive designs.

BY THOMAS OLSSON, JUKKA HUHTAMÄKI, AND HANNU KÄRKKÄINEN

key insights

˽ Professional Social Matching (PSM) is an emergent and potentially very impactful area of social matching systems, building on recommender systems, decision-support systems, social network analysis, and machine learning.

˽ Mindful of the ethics of computationally influencing professional matching activities, such as team formation and networking, the current computational approaches for profiling, matching, and recommending actors must be reconsidered.

˽ Future PSM systems should aim to enable unexpected encounters and social serendipity, incorporate a systemic perspective to the matching logic, help avoid human bias in decision-making, and identify optimal similarity-diversity trade-offs between actors.

˽ We argue that future PSM systems must be calibrated for different matching cases rather than for individual users; be based on multidimensional analytics of profiles and contextual data; support meaningful cooperation with the end user; and, feature proactive nudging to help the users avoid inherent human prejudice.