R P Sundarraj

With the proliferation of new innovative technologies, electronic gadgets like smartphones loaded with sophisticated user friendly apps are getting popularity among large proportion of phone users. As a result, advertisers are using apps as a medium of communication to showcase their products through in-app advertising. These advertisements are managed by ad aggregators through DSPs & by ad-space aggregators through SSPs. We need to allow most profitable ads to be pushed into appropriate apps so that overall SSP's revenue is maximized.Inapp advertising helps both the SSPs as well as DSPs to earn revenue. In this paper, we aim to maximize the revenue of SSP by developing machine learning based rule inferring technique from the output of mathematical model. Our initial computational results show the efficacy of online algorithm over previous research.

Today, smartphone-based in-app advertisement forms a substantial portion of the online advertising market. In-app publishers go through ad-space aggregators known as Supply-Side Platforms (SSPs), who, in turn, act as intermediaries for ad-agency aggregators known as demand-side platforms. The SSPs face the twin issue of making ad placement decisions within an order of milliseconds, even though their revenue streams can be optimized only by a careful selection of ads that elicit appropriate user responses regarding impressions, clicks, and conversions. This article considers the SSP's perspective and presents an online algorithm that balances these two issues. Our experimental results indicate that the decision-making time generally ranges between 20 ms and 50 ms and accuracy from 1% to 10%. Further, we conduct statistical analysis comparing the theoretical complexity of the online algorithm with its empirical performance. Empirically, we observe that the time is directly proportional to the number of incoming ads and the number of online rules.