  Mobile geo-location advertising, where mobile ads are targeted based on a
user's location, has been identified as a key growth factor for the mobile
market. As with online advertising, a crucial ingredient for their success is
the development of effective economic mechanisms. An important difference is
that mobile ads are shown sequentially over time and information about the user
can be learned based on their movements. Furthermore, ads need to be shown
selectively to prevent ad fatigue. To this end, we introduce, for the first
time, a user model and suitable economic mechanisms which take these factors
into account. Specifically, we design two truthful mechanisms which produce an
advertisement plan based on the user's movements. One mechanism is allocatively
efficient, but requires exponential compute time in the worst case. The other
requires polynomial time, but is not allocatively efficient. Finally, we
experimentally evaluate the tradeoff between compute time and efficiency of our
mechanisms.
