The 6th RecSysNL meetup will be on Wednesday September 28, 2016. The meetup will be hosted by ORTEC. The address is: Condensatorweg 54, 1014 AX Amsterdam. The meetup starts at 17:30.
Please note that you need to sign up for a free ticket to participate. Tickets are available at our Eventbrite page.
“Deep learning in recommender systems” by Alexandros Karatzoglou
Deep Learning (i.e. the return of Neural Networks part deux) is one of the most active and interesting areas in Machine Learning at the moment. New Deep Learning methods have shown to perform in several tasks in Image Processing, Natural Language Processing and Signal Processing. The emergence of these new Machine Learning methods creates new opportunities in the area of Recommender Systems, while at the moment there is relatively little work in the intersection of Deep Learning and Recommender Systems I will try to give an overview of the existing work, and also try to make some educated guesses about the future of Recommender Systems in light of the advancements in Deep Learning.
Senior Research Scientist at Telefonica Research in sunny Barcelona. I focus on several aspects of Machine Learning and Recommendation like Deep Learning, Learning to Rank and Context-awareness. Recipient of a two-year Marie Curie IEF fellowship award to undertake research on Context-aware Recommendation at Telefonica Research. My research in the area has been awarded with three best-paper awards at ECML/PKDD 2013, RecSys 2012 and ECML 2008.
Apart from mentoring PhD students at Telefonica Research, I am currently teaching courses on “Deep Learning” and “Machine Learning for Recommender Systems” at the UPF Graduate School of Economics Masters Course in Data Science in Barcelona and at the GSE Data Science Summer School.
I’m also the author of kernlab, a fairly popular Machine Learning package for R, and of CoFiRank, a collection of algorithms in C++ for Collaborative Ranking.
I received my PhD in Machine Learning from the Vienna University of Technology, while also being a frequent visitor at the Statistical Machine Learning group at NICTA in Canberra, Australia.
In my spare time, I enjoy kite-surfing, snowboarding, riding my bicycle, or learning to dance lindy hop.
“NewsREEL challenge: online evaluation of recommender systems” by Andreas Lommatzsch
Traditionally, the evaluation of recommender algorithms focuses on the offline evaluation using static, set-based datasets. For many web-based application scenarios the “static” evaluation does fit the characteristics of the scenario.
The News Recommendation Lab (NewsREEL) challenge wants to encourage researchers to focus on the more realistic online evaluation of recommender algorithms. The NewsREEL challenge allows researchers to evaluate algorithms for recommending online news articles. The algorithms are analyzed both online and offline taking into account the recommendation precision as well as technical aspects.
The talk explains the NewsREEL challenge in detail: We discuss the addressed challenges, review the implemented approaches, and explain our experiences gained organizing the challenge.
Dr. A. Lommatzsch received his PhD degree in Computer Science from the Berlin Institute of Technology. His research focuses on distributed knowledge management and information retrieval and systems. Since 2009 he has been a senior researcher and project coordinator in the domain of Recommender Systems and Machine Learning. His primary interests lie in the areas of recommendations based on data-streams and context-aware recommender algorithms.
“Recommendation challenges on Xing” by Daniel Kohlsdorf
XING is a social network for business. People use XING, for example, to find a
job and recruiters use XING to find the right candidate for a job. At the moment,
XING has more than 15 Million users and around 1 Million job postings on the
Among other things, Xing’s data science team works on job recommendations.
In this talk we will introduce the job recommendation problem as well
as Xing’s ongoing work on selected components of our recommender system.
Topics will include content based recommendations with latent spaces,
outlier filtering with ensemble methods. Furthermore, we
will describe the recommender systems challenge 2016 and how
our team created a semi-synthetic dataset released to the public.
Daniel is a Data Scientist at XING with a focus on data mining projects
and recommender systems. His research interests include automated behavior
analysis of humans and dolphins, contextual computing and data mining
with the application to recommender systems. At XING, he is developing
large-scale recommender systems that serve millions of users and handle
hundreds of requests per second. He received his PhD
in computer science from Georgia Tech with a specialization in machine
learning and computational perception and a masters degree in informatics
from University Bremen. He is an organizer of the RecSys Challenge at
ACM RecSys 2016 on job recommendation.