Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest? In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task or need. The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.
Koolen, M., Mobasher, B., Bogers, T., & Tuzhilin, A. (2019). Third workshop on recommendation in complex scenarios (ComplexreC 2019). In CEUR Workshop Proceedings (Vol. 2449, pp. 1–3). CEUR-WS.