![](bg8.png)
Figure 6: Distribution of visit probabilities.
fairly freely throughout the whole state. Even weather
conditionsdo not significantly change the visitors’ be-
havior. It is possible that this surprising behavior is
caused by the fact that the ”K¨arnten Card” is a bonus
card with free entry. People might tend to visit all ma-
jor and most expensive POIs they can. This behavior
can also be seen in the total distribution of visits as
shown in Figure 6. This figure does also explain why
a simple hoarding by global visit probability already
produced good results. People tend to visit the top
20 POIs. What else of the other 90 POIs is visited
cannot be predicted with the observed context. The
good news is that initial hoarding for the tour guide
can achieve good hit rates. In contrast to our initial as-
sumptions the region where the user stays for vacation
has a comparably low influence on the visit probabil-
ities and thus cannot be used for automatic hoarding
in our scenario. We therefore suggest the implemen-
tation of a Web platform on which the user can take
the hoarding decision manually. This has the addi-
tional advantage that no mobile hoarding infrastruc-
ture (WLAN hot spots) needs to be installed. Such
a Web platform could be integrated into the official
tourist web-site of a large area (such as Carinthia in
our case). It should be used to identify the relevant
POIs for the user and to acquire the user and device
context to build a customized tour guide.
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