Theory & Engineering of Large E-commerce Applications

Come join us at our next meetup featuring 2 great speakers. Presentations start at 7PM. Appetizers and drinks will be served!

Jiliang Tang - Assistant professor in the Computer Science and Engineering at Michigan State University

Abstract: Deep Reinforcement Learning (DRL) is able to automatically learn the optimal recommendation strategies by capturing real-time users’ feedback and maximizing the expected long-term cumulative benefit from users. In this talk, Prof. Tang will delineate challenges of adopting DRL to recommendations, and detail two representative works from his group: (1) how to jointly optimize recommendation and display strategies in a page and (2) how to capture negative feedback. Some promising directions will be further discussed to bring this research direction into a new frontier.

David Haubenstricker - Staff Data Engineer at Criteo

Abstract: Big-data Spark applications quickly grow to become nearly unreadable and untestable. This presentation will show some simple patterns that will take a whiteboard diagram of a complex process and convert it to readable Spark code, complete with unit tests. Spark experience not necessary. Many of the patterns have application beyond Spark programming.