Feynman: Federated Learning-Based Advertising for Ecosystems-Oriented Mobile Apps Recommendation

While recommender systems have been ubiquitously used in digital marketing and online business development, the conversions of online advertising for mobile apps installation and activation sometimes are far from satisfactory, due to the lack of feedback from App-related activities, leading to a poor record of Return on Investment (RoI). Though the advertisers, e.g., App operators and App Store, are granted to log users’ app-related activities such as installation, activation, usages, and preferences per the agreement, they usually limit the access to such data from advertisement publishers, due to the privacy concerns. To improve conversions of online advertising under privacy controls, we propose
Feynman
—a
fe
derated learning-based advertising platform for ecos
y
stems-orie
n
ted
m
obile
a
pps recommendatio
n
.
Feynman
aims at improving the RoI of mobile app recommendation from an ecosystem's perspective, i.e., per investment in advertising an app (
Goal. 1
) increasing the number of new installs/users of the app, and then (
Goal. 2
) increasing the number of new active users (preferably with frequent in-app purchase activities). Incorporating with a federated computing platform,
Feynman
leverages users’ records stored in advertisers to refine the pool of targeting users for ads distribution, and jointly builds the predictive models for users’ purchase activities forecasting using features from the Ads publisher and the advertiser. With refined target pools and more accurate models,
Feynman
has successfully helped several mobile apps in China by attracting more than 100 million users to further enlarge their user populations and revenues from in-app purchases. Note that rather than proposing new techniques for federated learning, the design of
Feynman
dedicates to show its promising performance in the industrial practices of advertising using federated computing and privacy protected strategies. In three cases that we report in this paper,
Feynman
outperforms the state-of-the-art plans in terms of several key measurements, including Click-Through Rates (CTR), Conversion Rate (CVR), Cost per Action (CPA), and Non-targeting User Hit-Rates (NTHR).