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.