  Social collaborative platforms such as GitHub and Stack Overflow have been
increasingly used to improve work productivity via collaborative efforts. To
improve user experiences in these platforms, it is desirable to have a
recommender system that can suggest not only items (e.g., a GitHub repository)
to a user, but also activities to be performed on the suggested items (e.g.,
forking a repository). To this end, we propose a new approach dubbed Keen2Act,
which decomposes the recommendation problem into two stages: the Keen and Act
steps. The Keen step identifies, for a given user, a (sub)set of items in which
he/she is likely to be interested. The Act step then recommends to the user
which activities to perform on the identified set of items. This decomposition
provides a practical approach to tackling complex activity recommendation tasks
while producing higher recommendation quality. We evaluate our proposed
approach using two real-world datasets and obtain promising results whereby
Keen2Act outperforms several baseline models.
