Identifying Short-term Interests from Mobile App Adoption Pattern

Bharat Gaind, Nitish Varshney, Shubham Goel, Akash Mondal


With the increase in an average user’s dependence on their mobile devices, the reliance on collecting user’s 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 user’s short-term interests and pushing relevant ads. However, the major limitation of such an extraction from mobile browsers is that browsing history gets reset when the browser is closed or when the device is shut down/restarted; thus rendering existing methods for identification of user’s short-term interests on mobile devices, ineffective. In this paper, we propose an alternative method to identify such short-term interests by analysing user’s 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.


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