Nowadays, researching online before booking a vacation can be seen as a common habit of customers. In this context, Recommender Systems (RSs) are aiming to support the customers to find the right products, but they face domain specific challenges since tourism products are typically very complex and related to emotional experiences. To counteract these challenges, comprehensive user models for capturing the preferences and personality of travelers have been introduced. One of these models is the so-called Seven-Factor Model. This work introduces an automated way for determining the Seven- Factor representation of tourism destinations and hotels to enable a matchmaking for RSs. In particular, exploratory data analyses, cluster analyses, and regression analyses are conducted not only to find a mapping of tourism destinations and hotels onto the Seven- Factors, but also to foster a better understanding of the relationship between destination attributes and the Seven-Factors, and between hotel attributes and the Seven-Factors. The main results show that conceptually meaningful groups of destinations and hotels as well can be identified and associated with the Seven-Factors, but they can only be used for direct allocations rather than for determining each factor of the Seven-Factor Model. Furthermore, the regression analyses provide clear evidence that a tourism destinations Seven-Factor representation and a hotels Seven-Factor representation can be determined by taking the respective attributes into account. In general, the quality of the developed models varies for different factors of the Seven-Factor Model and also for different tourism products (i.e., destination and hotels). Finally, the introduced approach can easily be followed for new data sources and product types.