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.