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