Modeling User Engagement Profiles for Detection of Digital Subscription Propensity
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
2022
chapter in monograph / paper
english
- big data
- user engagement modeling
- user profiling
- subscription for digital content
- streaming data processing
EN In this paper, we study how the application of a dynamic user engagement profiling can influence the efficiency of systems aimed at detecting the user’s propensity to buy a subscription. Specifically, we address a task of identifying the digital media readers who are involved enough in the publisher’s offer to pay for access to the content of a given webpage. We present the user engagement profile updating framework responsible for enriching raw events with time-agnostic temporal features. In particular, we experimentally evaluate the performance of machine learning algorithms for the task of predicting the user propensity to subscribe using the synthetic dataset based on publicly available data streams on users of KKBox’s music service. Additionally, we provide the results of online tests in which the propensity-to-subscribe prediction model is used to control the paywall displays on a digital media website with live traffic. The results of experiments have proven that enrichment of data with engagement profiles leads to higher performance of prediction models than relying just on raw features and tuning the model’s hyperparameters.
16.02.2022
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