A CrowdRec, Gravity and MTA STZAKI organized meetup, to take place on the 9th of June in Budapest, Hungary:
Fabian Abel (Data scientist at XING – the primary social network for business contacts in German-speaking countries, Germany; @fabianabel, http://fabianabel.de/) will talk about the some challenges at XING in conjunction with the RecsysChallenge 2016, co-organized by XING and SZTAKI
Title: What’s wrong with you, Recruiter-John? A non-trivial recommender challenge.
Maria from Illusion Inc. recently told her colleague Recruiter-John that she needs an additional team member: “I’m looking for a new team member that can support us in building up our new search engine. Some experience with Lucene or other search technologies and some background in information retrieval would be cool!”. Recruiter-John then did his work and redirected her message to the world: “We are searching for a Data Scientist who has a PhD in Computer Science or some related field, 5 years industry experience in building search engines with Lucene at large scale, is highly skilled in Elixir and Erlang and fits into our young, dynamic team…”. Paul is currently searching for a job: “I am a Senior Software Engineer with more than 10 years of industry experience in Java, Scala, C++ and 15 other programming languages, familiar with technologies such as Solr, Hadoop, Pig, Hive, Hbase, Cassandra, Riak, … I’m currently looking for a job where I can bring my dog to work.” Bringing together the demand and offer on the job market is a non-trivial problem. In this talk, we will share some of our frustration and will describe how we tackle this problem with recommender systems on XING, a career-oriented social network. We will give insights on the RecSys challenge (http://recsyschallenge.com) which is our attempt to let the bright minds of the RecSys community solve the recommendation problem that we failed to solve properly over the past four years.
Alexandros Karatzoglou (Researcher at Telefonica, Spain, @alexk_z, http://www.ci.tuwien.ac.at/~alexis/About.html), will talk about how to leverage deep learning technologies for recommender systems.
Title: Deep Learning for Recommender Systems
Summary: 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.
Guy Rapoport (Software Engineer at Dato, US) will talk about Graphlab Create and its recommendation capabilities.
Title: Practical Recommender Systems Tips
Summary: GraphLab Create is a Python library for scalable Machine Learning. In this talk we will explore different types of recommendation models, and describe how to use them in practice. Guy will talk about his experience in bringing personalization into existing businesses in many different verticals. This lecture will include practical tips and live examples.
Dato & GraphLab Training before the meetup
Please register here: