| Abstract | Much of the knowledge available on the web today comes asa result of fruitful collaborations among large groups of peo-
ple. One of the most striking examples of successful web col-
laboration is the online encyclopedia Wikipedia. The web is
used as a collaboration platform by highly specialized blog-
ging communities and by the scientific community. An im-
portant reason for the richness of content generated through
web collaborations is that the participants in such collabo-
rations are not constrained by geographic location. Thus,
like-minded individuals from across the world can join their
efforts. This also means, however, that web collaborators of-
ten do not know each other, and, thus, finding collaborators
on the web is more difficult than it is with more traditional
forms of collaboration that are initiated based on acquain-
tance. This difficulty is further exacerbated by the fact that
web collaborations tend to be more dynamic as participants
join and abandon communities. We consider the task of rec-
ommending project-specific potential collaborators to web
users and propose an approach that is based on statistical
relational learning. Our proposed model thus has the ad-
vantages that it can include complex features composed of
multiple properties and relationships of the entities, it can
handle the high levels of noise and uncertainty inherent in
user actions, and it allows for joint decision-making, which
leads to more accurate predictions. To ensure scalability,
our model is trained in an online fashion. We demonstrate
the effectiveness of our approach on a data set collected from
Wikipedia.
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