different domains of work. For example,
the highly dynamic environment of a
startup company highlights the importance of strong ties as high-bandwidth
sources of relevant information. At
the other end of the spectrum, an academic research group produces new
knowledge over years rather than days.
Therefore, the actors are able to invest
in utilizing low-bandwidth weak ties
due to their potential in serving non-redundant, novel knowledge.
In sum, many of the these aspects
represent largely unobservable contextual factors that are subjectively
defined. This forms a cyclical relationship between context and collaboration where the collaboration activity
gives rise to context and the context influences activity. 1 Moreover, the context forms as a mixture of several individuals’ and organizations’ contextual
characteristics. Formalizing this contextual variance requires new methods
and frameworks for conceptualizing
PSM in more detail. This also means
the plethora of data needed to reach
certain analytical targets is unpractical
without new mechanisms of granting
access to personal data. PSM development endeavors need to carefully consider which practically available data
can best support the context-specific
analytical goals and how to compensate missing data.
We suggest there are two alternative
design strategies to enable context sen-
sitivity in PSM systems. Reactive design
ies in introducing “disconnected in-
dividuals or facilitating new coordina-
tion between connected individuals.” 28
Utilizing social proxies can be expected
to facilitate building trust between the
apparently diverse actors.
PSM System Qualities
and Future Directions
To address the aforementioned goals,
we introduce a model of the analytical
elements that we consider crucial for
future PSM systems. Figure 4 outlines a
high-level system architecture with key
modules, inspired by Terveen, 36 and
their main functions. These are related
to an analytics pipeline as well as general system qualities that need to be
addressed across the presented modules. With this overview, we underline
key requirements for future work and
relevant research directions.
Sensitivity for professional contexts
and purposes. Perhaps the most ana-
lytically challenging requirement is
that systems should model not only
the potential actors and their social
structure but also the context of the in-
tended professional relationship. The
produced models should drive tailor-
ing of the matching logic accordingly
(for example, changing priorities and
weights of variables). The notions of
context and context-awareness have
been extensively discussed across the
sciences, and various contextual vari-
ables have been investigated in rela-
tion to information systems in general
and recommender systems in particu-
lar (for example, location, culture, user
personality, task context). 1 In what fol-
lows, we focus on a few aspects that are
particularly relevant when considering
the matching logic.
The quest for a sweet spot between
maximal diversity and similarity is already challenging per se. Furthermore,
it is a moving, context-dependent target. The optimal degree of diversity depends on individuals and the targeted
collaboration in question. For example,
the end user’s personality has been
found to affect the readiness for accepting diversity of recommendations in
content and item recommenders, 41 and
we expect that such inherent human
factors also have an equally significant role in people recommenders. As
for organizations, characteristics like
openness, tolerance of difference, and
the different purposes of collaboration
presumably also have an effect.
Similarly, an understanding of the
current social structures is needed to
identify meaningful configurations of
the matching logic. Our premise is that
a typical structure is composed of clusters that are densely interconnected
but loosely connected to other clusters. Consequently, we suggest that
PSM developers draw from the diversity-bandwidth trade-off theory4
to navigate the design space. Ties
of different strengths (weak–strong)
all have potential as conduits of novel
information but in different ways in
Figure 5. Outlining relevant user interactions in the human-in-the-loop analytics process. Typical research and design challenges in italics.
Interactions and reasoning
User Interactions and Inputs
Analytics Process
Goals and Data
Data Curation Profiling Parallel methods
for matching logic
Presentation
Defining the matching case
to initialize the process:
• Parameter tuning
• Adding critical missing data
Data enrichment
• Labeling clusters
• Refining the user model
Interactive sensemaking
and decision-making:
• Tagging and bookmarking the results
• Filtering and prioritizing
• Recalibrating visualizations
Labels and refinement
Unclear goals
Conflicting ideals
Cold start problem
Choice of relevant factors
Opacity and trust issues
Communicating uncertainty
Meaningful system agency