Persuasion can also manifest itself
via facilitation of forming the connection. For example, in an event context
with low-risk matching, the matched
actors can be given a playful challenge to explore the commonalities
and complementariness by not revealing the exact reason behind the
recommendation, such as in Chen et
al. 8 Here, the human capabilities in
identifying possible matches could
also be harnessed beyond the primary
end user, for example, encouraging a
third party to serve as a matchmaker
between people they know, thus facilitating newly recommended connections and validating the ill-reasoned
recommendations that an algorithm
Social matching is ubiquitous in professional life, yet suboptimally supported by computational systems.
Based on a multidisciplinary review of
the literature, we outline the complex
problem space of Professional Social
Matching, and we define design goals
for computer-supported PSM and requirements for developing next-generation systems.
We suggest that many conventional
approaches in recommender systems
and social link prediction, when applied to PSM, could have detrimental
long-term implications for organizations’ and individuals’ performance.
Conventional mechanisms, such as
optimizing for similarity and triadic
closure, involve risks of strengthening
the human biases of homophily and
echo chambering. We call for diversity-enhancing and contextually sensitive
designs of future PSM systems, tapping on multi-dimensional analytics
of not only the potential matched actors but also the intended type of collaboration and organizational context.
Further, we call for a paradigmatic
shift from automated recommender
systems toward decision-support systems that are based on meaningful
All in all, analysis of this application
area underlines the importance and urgency of rethinking some paradigmatic
algorithmic approaches. In such a complex and interdisciplinary area as PSM,
reconsidering the conventions is not
only beneficial; it is a necessity.
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