  We introduce an approach to recommending short-lived dynamic packages for
golf booking services. Two challenges are addressed in this work. The first is
the short life of the items, which puts the system in a state of a permanent
cold start. The second is the uninformative nature of the package attributes,
which makes clustering or figuring latent packages challenging. Although such
settings are fairly pervasive, they have not been studied in traditional
recommendation research, and there is thus a call for original approaches for
recommender systems. In this paper, we introduce a hybrid method that leverages
user analysis and its relation to the packages, as well as package pricing and
environmental analysis, and traditional collaborative filtering. The proposed
approach achieved appreciable improvement in precision compared with baselines.
