for example, in terms of the breadth of given alternatives (variety of matches), the need for transparency and explanatory capability of the logic behind recommendations, and the agency and role that a system has in the decision-making. In Figure 2, the recommendations related to increased complexity and cost of failure imply higher risks and require increased explanatory power of recommendations from PSM systems. Deciding whom to meet at an event has low risks as the interaction would be short term and of low intensity; therefore, a user might be satisfied with only a few algorithmic recommendations and superficial reasoning behind the recommendations. However, matching for which actor to choose for close business collaboration poses higher risks and costs of failure. For the user to trust and follow the recommendations, this requires both more in-depth algorithmic reasoning and better explanation capabilities in the user interface.

Pitfalls in Computational PSM

A key argument of this article is the fundaments of state-of-the-art technologies and approaches that could be utilized to build PSM systems—for example, item recommenders, social network analysis, and machine learning—must be reconsidered in this application area. Directly applying the prevailing analysis or design patterns to PSM can introduce new risks with detrimental effects on the performance and collaboration practices of knowledge workers. The following critically reviews the suitability of common computational approaches.

First, it is imperative to realize that
recommendation does not equal pre-
diction, 26 particularly in PSM. To truly
enhance professional collaboration,
recommender algorithms should not
reproduce or strengthen the biased
human behavior. For example, in a
recruitment system, using prior exam-
ples as training data for machine learn-
ing is expected to strengthen the de-
mographic distribution that a certain
organization or profession has tradi-
tionally had. As McNee et al. 26 pointed
out, an accurate recommender engine
might produce recommendations that
are formally relevant as predictions, yet
not very useful as recommendations
(for example, a well-connected person
is recommended to most users).
Wallach gave a gender-related ex-
ample of the same issue: “There is a sub-
stantial difference between a model that
is 95% accurate because of noise and one
that is 95% accurate because it performs
perfectly for white men, but achieves
only 50% accuracy when making predic-
tions about women and minorities.” 39
Similarly, it would be straightforward
to predict who someone might meet
at an event based on their history of
professional social encounters in simi-
lar situations. However, using that as
a recommendation would strengthen
their habitual behavior, which might be
against their actual collaboration needs.
Second, social matching systems
tend to look for maximal similarity; in
fact, dating scenarios have particularly
been found to benefit from emotional
similarity. 13 A perfect match in dat-
ing services refers to the similarity of
profiles, typically based on analyzing
a simple user-defined, property-based
profile content and clustering the pool
of actors. This logic also seems to have
affected the current vocational match-
ing services: shared qualities tend to be
highlighted in the user interface, and
the brevity of profiles can lead the user
to follow the natural tendency of seek-
ing for similarity.

Third, from the perspective of social ties and networks, contemporary

Figure 1. Overview of relevant scientific domains, concepts, and research areas to develop next-generation computer-supported PSM.

Computer-Supported
Professional
Social Matching

Decision-Support Systems
People Recommender Systems

Social Sciences
Social ties
Behavioral bias
Social serendipity
Computational social science
Management
Sciences

Decision-making
Information systems
Knowledge work in
the digital age
Network theory and
social network analysis
Human-computer interaction
User and topic modeling
Prescriptive analytics
Machine learning
Data science

Computational Sciences

Figure 2. Main tracks of PSM, with examples of matching cases with different scales of cost upon a suboptimal matching decision. The complexity of matching decision-making increases as the number of actors increase.

Mentoring
Academic
partnering
Establishing
a startup
Consultancy
Extending
a team
Recruitment and
headhunting
Forming a
new team
Business
partnering
Ecosystem
players

1-to-many 1-to- 1 many-to-many

C
os
t
o
f
Failur
e
Complexity