An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. Most existing works on the topic have focused on a specific application domain, thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we discuss a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for every package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. the target user's preferences, possibly including a limited budget. Our developed method has been tested on the travel package domain using a TripAdvisor dataset.