Classical recommender systems provide users with ranked lists of recommendations that are relevant to their preferences. Each recommendation consists of a single item, e.g., a movie or a book. However, these ranked lists are not suitable for applications such as travel planning, which deal with heterogeneous items. In fact, in such applications, there is a need to recommend packages the user can choose from, each package being a set of Points of Interest (POIs), e.g., museums, parks, monuments, etc. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of POIs that may constitute a tour. Given a collection of POIs, where each POI has a cost and a time associated with it, and the user specifying a maximum total value for both the cost and the time (budgets), our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. We introduce a scoring function and propose a ranking algorithm that takes into account the preferences of the user, the diversity of POIs included in the package, as well as the popularity of POIs in the package. Extensive experimental evaluation of our proposed system, using a real dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.