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