the characteristics of PSM. To turn the
focus from critique toward constructive thinking, we next propose high-level goals for the future generations
of PSM systems. These are intended to
advance this nascent research area toward socially meaningful and ethically
sustainable design directions.
Goal: Balance diversity and similarity. Comparing to leisurely social
matching applications or content recommenders, we argue that PSM systems should employ more diversity-enhancing approaches that shift the
current aim for convergence toward
divergence. While diversification has
been recognized as a relevant aim for
recommender systems in general, 23
matching people for professional collaboration makes a particularly strong
case for this.
At the same time, neither optimizing for similarity nor diversity should
be taken as a maxim: the optimum is
a moving goal somewhere between
these extremes. Successful collaboration requires mutual trust and shared
working culture (that is, similarity as
a consolidating, convergent power) as
well as openness for change and different perspectives (that is, diversity as a
divergent power). Similarity is useful
when the aim is to validate ideas or
methods and there is a need for agility in short-term collaboration. Strong
common denominators can also open
minds to appreciate individual differences. Diversity is beneficial in developing novel ideas with the help of complementary perspectives, establishing
long-term business ventures utilizing
complementary social capital, or making well-informed strategic decisions
based on diverse knowledge.
Identifying the optimal balance requires analysis and modeling of not
only the actors but also the characteristics of the intended collaboration and
the collaboration context. Compared
to leisurely matching, professional life
is more dynamic in terms of interaction needs, interests, and resources at
different times.
Goal: Enable experiences of social
serendipity. We call for systems that
help people make professional match-
ing decisions with positive long-term
benefit. We argue that the experience
of serendipity is an indicator of success-
ful knowledge work, making it a desir-
approaches for analyzing connections
between individuals often utilize so-
cial network analysis and link predic-
tion. 24 Following the triadic closure
hypothesis, new ties are more likely to
be formed between friends-of-friends
or colleagues-of-colleagues, 10 that is,
between actors that share a strong
connection. The triadic closure mech-
anism can, however, enforce echo
chambers and increase polarization—
the typical pitfalls of social networking
services in the 2010s. In knowledge
work, we argue the narrowed think-
ing due to echo chambers is bound to
reduce exposure to novel information
and decrease divergent thinking and
innovation capability.
Fourth, PSM system designers must
consider that good matches cannot be
generalized across individuals. In con-
tent or item recommender systems,
the same news article or product is of-
ten recommended to several users with
corresponding consumption behav-
ior and ratings—that is, collaborative
filtering. 20 However, in PSM, person
A being a good partner for person B
does not imply that A would also be a
good match for person C, even if B and
C had similar qualities. An optimal
match in professional life is very case
specific and determined by, among
other things, the matched actors’ cur-
rent needs, interests, personality, and
availability for collaboration. Matches
can be generalized only across simi-
lar cases. This means that narrowing
down only to similar collaboration cas-
es can lead to data sparsity and that the
collaborative filtering approach would
suffer from cold-start issues.
Finally, as professional activities are
related to value creation for organizations—and, more broadly, communities
and societies—PSM calls for a systemic
perspective. For example, the same central and active individuals cannot practically be recommended to everybody
(that is, the Matthew effect). The mechanism of preferential attachment5 tends
to lead to power-law distribution across
the population. A system might recommend excessive collaboration opportunities for people that already have plenty
of connections while undervaluing other criteria, such as urgency of the need
for collaboration or the actors’ practical
capacities for exploring new collaboration opportunities.
New Design Directions
The limitations in human decision-making and the pitfalls in the traditional computational approaches essentially imply that PSM is far from trivial
bulk predictions targeted to masses.
Neither the matching mechanisms
in dating applications nor the analytics methods in contemporary content
recommender systems and link prediction algorithms seem to fit with
Figure 3. Illustrative example of matchmaking potential within a community.
Nodes represent actors that
are interconnected through
collaboration, and node
size represents similarity
between actors’ interests.
Several existing clusters of
actors similar to the active
user (in red) are identifiable
either computationally
or manually through an
interactive visualization.