  With the increase in an average user's dependence on their mobile devices,
the reliance on collecting his browsing history from mobile browsers has also
increased. This browsing history is highly utilized in the advertising industry
for providing targeted ads in the purview of inferring his short-term interests
and pushing relevant ads. However, the major limitation of such an extraction
from mobile browsers is that they reset when the browser is closed or when the
device is shut down/restarted; thus rendering existing methods to identify the
user's short-term interests on mobile devices users, ineffective. In this
paper, we propose an alternative method to identify such short-term interests
by analysing their mobile app adoption (installation/uninstallation) patterns
over a period of time. Such a method can be highly effective in pinpointing the
user's ephemeral inclinations like buying/renting an apartment, buying/selling
a car or a sudden increased interest in shopping (possibly due to a recent
salary bonus, he received). Subsequently, these derived interests are also used
for targeted experiments. Our experiments result in up to 93.68% higher
click-through rate in comparison to the ads shown without any user-interest
knowledge. Also, up to 51% higher revenue in the long term is expected as a
result of the application of our proposed algorithm.
