  Making personalized recommendation for cold-start users, who only have a few
interaction histories, is a challenging problem in recommendation systems.
Recent works leverage hypernetworks to directly map user interaction histories
to user-specific parameters, which are then used to modulate predictor by
feature-wise linear modulation function. These works obtain the
state-of-the-art performance. However, the physical meaning of scaling and
shifting in recommendation data is unclear. Instead of using a fixed modulation
function and deciding modulation position by expertise, we propose a modulation
framework called ColdNAS for user cold-start problem, where we look for proper
modulation structure, including function and position, via neural architecture
search. We design a search space which covers broad models and theoretically
prove that this search space can be transformed to a much smaller space,
enabling an efficient and robust one-shot search algorithm. Extensive
experimental results on benchmark datasets show that ColdNAS consistently
performs the best. We observe that different modulation functions lead to the
best performance on different datasets, which validates the necessity of
designing a searching-based method.
