#MLDublin meets @Zalando

Meet up number #24. ML Dublin

AGENDA:

[18:00 - 18:30] Registration, Networking

[18:30 - 18:40] Introduction and welcome

[18:40 - 19:10] Prof. Joeran Beel: The Potential of Recommender-Systems at Macro- and Micro Level

We introduce the concept of a “macro recommender system”, and “micro recommender system” respectively. Both systems can be considered as recommender systems for recommendation algorithms. A macro recommender system recommends the potentially best performing recommendation algorithm to an organization that wants to build a recommender system. This way, an organization does not need to test dozens or even more algorithms to find the best one for their particular platform. A micro recommender system recommends the potentially best-performing algorithm for each individual recommendation request. This concept is based on the premise that there is no single-best algorithm for all type of users, items, and contexts.

[19:15 - 19:35] Sergio Gonzalez Sanz (@S_Gonzalez_S)
Senior Research Engineer / Data Scientist @ Customer Data Platform, Zalando

"Personalisation of the Zalando Web Experience using customer data in real time with Cassandra and Flink"

[19:35 - 19:55] Alice Lynch (@alice82177)
Research Engineer / Data Scientist @ Customer Data Platform, Zalando

"Predicting Customer Spend Behaviour for Personalisation of the Zalando Web Experience using SparkML"

Zalando aims to provide a relevant and personal shopping experience for each individual customer. Understanding and predicting customer spending patterns allows us to suggest the most relevant products from our huge assortment, at the right price point for the individual.
I'll discuss how we have implemented a customer spending prediction model using SparkML and our system to ship machine learning to production at scale.

[20:00 - 20:55] Networking

IMPORTANT NOTES:

This event is strictly for Machine Learning professionals, researchers and students only

• If you finally can't make it, please RSVP to "NO" as soon as possible so that other people can take your place.

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