Apriori Rule--Based In-App Ad Selection Online Algorithm for Improving Supply-Side Platform Revenues

Abstract

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.

In-app advertising: a systematic literature review and implications for future research

“…Scholars have examined techniques to improve ad auction mechanisms (Liu and Liu, 2019) and optimal revenue-sharing methods (Hao et al , 2016; Hu et al , 2023). Mukherjee et al (2017) develop a machine learning-based in-app ad selection algorithm and propose an optimization model to maximize supply-side platform revenue.…”

Header Bidding as Smart Service for Selling Ads in the Digital Era

“…Supply-side platforms analyze the available ad impressions against their advertisers' target group (Gonzalvez and Mochon, 2016). They select the highest bid from their advertisers and forward it in real time to the header bidding wrapper for inclusion in the auction (Mukherjee et al, 2017). By increasing the number of potential buyers for ad spaces, supply-side platforms enable publishers to realize higher prices (Marshall, 2014).…”

Implementasi Algoritma a Priori Untuk Menghindari Pembelian Valuta Asing Yang Beresiko Turun Bersamaan

“…Mukherjee menggunakan a priori untuk mempertimbangkan perspektif Supply-Side Platforms (SSP) penyedia iklan. Hasil penelitian ini menunjukan jika waktu pengambilan keputusan berbanding lurus dengan jumlah iklan yang masuk dan jumlah aturan [2]. Sharma melakukan perbaikan Association Rule untuk prediksi penugasan bug dari bug yang baru dilaporkan menggunakan atribut bug yang berbeda, yaitu tingkat keparahan, prioritas, komponen dan sistem operasi [3].…”