This paper introduces the concept of "smart routing" as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour's travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The "smart routing" approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system's ability to reflect on the tourists' dynamic preferences, for which an understanding of the influence of a tourist's affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour's cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist's preferences and dynamic needs. As a result, each tour can be personalised in a "smart" way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the "smart routing" concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist's affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard.

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Localisation has become a standard feature in many mobile applications. Numerous techniques for both indoor and outdoor location tracking are available today, providing a diversity of ways positioning information can be delivered to a mobile application (e.g., a location-based service). Such factors as the variation of precision over time and covered areas or the difference in quality and reliability make the adoption of several techniques for one application cumbersome. This work presents an approach that models the capabilities of localisation systems and then uses this model to build a unified view on localisation, with special attention paid to uncertainty coming from different localisation conditions and its presentation to the user. We discuss technical considerations, challenges and issues of the approach and report about a user study on users' acceptance of the suggested behaviour of an application based on the approach. The results of the study showed the feasibility of the approach and revealed users' preference towards automatic but yet informed changes they experienced while using the application.

In determining the selection of sites to visit on a trip tourists have to trade-off attraction values against routing and time-use characteristics of points of interest (POIs). For recommending optimal personalized travel plans an accurate assessment of how users make these trade-offs is important. In this paper we report the results of a study conducted to estimate a user model for travel recommender systems. The proposed model is part of c-Space-a tour-recommender system for tourists on a city trip which uses the LATUS algorithm to find personalized optimal tours. The model takes into account a multi-attribute utility function of POIs as well as dynamic needs of persons on a trip. A stated choice experiment is designed where the current need is manipulated as a context variable and activity choice alternatives are varied. A random sample of 316 individuals participated in the on-line survey. A latent-class analysis shows that significant differences exist between tourists in terms of how they make the trade-offs between the factors and respond to needs. The estimation results provide the parameters of a multi-class user model that can be used for travel recommender systems.

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