Personalized recommendations in a mobile tourist information system suffer from a number of limitations. Most pronounced is the amount of initial user information needed to build a user model. In this paper, we adopt and extend the basic concepts of recommendation paradigms by exploiting a user’s personal information (e.g., preferences, travel histories) to replace the missing information. The designed algorithms are embedded as recommendation services in our TIP prototype. We report on the results of our analysis regarding effectiveness and performance of the recommendation algorithms. We show how a number of limiting factors were successfully eliminated by our new recommender strategies.