# Identifying short-term interests from mobile app adoption pattern

## TL;DR

This paper introduces a novel approach to infer users' short-term interests on mobile devices by analyzing app adoption patterns, overcoming limitations of browsing history-based methods, and demonstrates significant improvements in ad engagement and revenue.

## Contribution

The paper presents a new method for identifying short-term interests through app adoption patterns, enhancing targeted advertising effectiveness on mobile devices.

## Key findings

- Up to 93.68% higher click-through rate with interest-based ads.
- Up to 51% increase in long-term revenue.
- Effective identification of ephemeral user interests.

## Abstract

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.