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T. Jatschka, T. Rodemann, G. Raidl:
"Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications";
Talk: International Conference on Machine Learning, Optimization, and Data Science, Certosa di Pontignano, Siena, Italy; 2019-09-10 - 2019-09-13; in: "Machine Learning, Optimization, and Data Science", Lecture Notes in Computer Science, 11943 (2019), ISBN: 978-3-030-37599-7; 738 - 750.

In this contribution we address scaling issues of our previously
proposed cooperative optimization approach (COA) for distributing
service points for mobility applications in a geographical area. COA
is an iterative algorithm that solves the problem by combining an optimization
component with user interaction on a large scale and a machine
learning component that provides the objective function for the optimization.
In each iteration candidate solutions are generated, suggested to the
future potential users for evaluation, the machine learning component is
trained on the basis of the collected feedback, and the optimization is
used to nd a new solution tting the needs of the users as good as
possible. While the former concept study showed promising results for
small instances, the number of users that could be considered was quite
limited and each user had to evaluate a relatively large number of candidate
solutions. Here we deviate from this previous approach by using
matrix factorization as central machine learning component in order to
identify and exploit similar needs of many users. Furthermore, instead of
the black-box optimization we are now able to apply mixed integer linear
programming to obtain a best solution in each iteration. While being
still a conceptual study, experimental simulation results clearly indicate
that the approach works in the intended way and scales better to more
users.

https://publik.tuwien.ac.at/files/publik_288238.pdf