Recommendation engines are the software that suggests what we should watch or read or listen to next. They help us deal with the millions of choices the Web offers. But can a software really make good suggestions that really are of interest to us?
XING is a social network that aims at enabling professionals grow. In today's main talk, Katja, Data Scientist at XING will give some insights into the machine learning pipelines that XING uses for building recommender systems. She will focus on job recommendations and discuss challenges, architecture, features and algorithms that is used for recommending job ads to people and for understanding whether a person is actually willing to change jobs and an appropriate candidate for a given job.
18:30 Doors open
19:00 Welcome (Anja Schumann, Women Techmakers Hamburg)
19:10 Recommendations at XING SE: From Theory to Production (Dr. Katja Niemann, Data Scientist, Xing SE)
20:00 Snacks & Networking
Dr. Katja Niemann
Katja’s main interest lies in making applications more beneficial and supportive for the users. To do so, she uses state-of-the-art machine learning approaches to better understand and assist the users’ needs. She joined the XING AG in Hamburg as data scientist in March 2016. Before, she was working as a researcher at the Fraunhofer Institute for Applied Information Technology (FIT) where she focused on improving the learning experience for online learners and did her PhD at the RWTH Aachen in the area of data mining and recommender systems.