How mobile.de brings Data Science to Production for a Personalized Web Experience

Dr. Florian Wilhelm (inovex) und Dr. Markus Schüler (mobile.de) mit ihrem (englischen) Vortrag am 8. Juli 2018 auf der PyData Berlin.


As Germany's biggest online car marketplace, mobile.de provides a personalized web experience. Our Data Team leverages the interactions of our users to infer their preferences. For this tasks we often apply Python and Spark to wrangle massive amounts of data. In this talk, we are going to present our personalization use-cases as well as the application of PySpark in production.


After a short introduction we will present various data use cases that where tackled by mobile.de, Germany's largest vehicle marketplace online, in the last two years. In a combined endeavour inovex, an IT project house with a strong focus on digitalization, has supported mobile.de on this voyage.

Personalized web experience is a commonly used term in e-commerce that is hard to grasp. Thus, we illustrate how mobile.de understands personalized web experience and outline its features as well as opportunities. In more detail, we will elaborate on the Bayesian framework that we use to approximate user preferences. Furthermore, we discuss the modelling of user intent and how this can be used to understand the buyer's journey.

Besides the data science and modelling aspects of the use cases we will also dive into the technical details and how we solved them with the help of Python and Spark (PySpark). In more detail we will address the implementation of efficient User-Defined-(Aggregation)-Functions with Pandas in PySpark as well as the management of isolated environments and dependencies with PySpark.

We will conclude our talk with the benefits of a personalized web experience for the users of mobile.de which was achieved with the help of Python and PySpark in production.

Speaker: Dr. Florian Wilhelm (inovex), Dr. Markus Schüler (mobile.de)

Event: PyData 2018

Datum: 08.07.2018

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Dr. Florian Wilhelm

Dr. Florian Wilhelm ist Data Scientist bei inovex. Er verfügt über mehrjährige Projekterfahrung im Bereich Predictive & Prescriptive Analytics und Big Data und über fundierte Kenntnisse in den Bereichen mathematische Modellierung, Statistik, maschinelles Lernen, Hochleistungsrechnen und Data Mining.

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