The 3rd RecSys NL meetup will be on Monday 8 Dec at SDL Amsterdam. There will be four great speakers from Xing, TextKernel, CoolBlue, and UvA (LivingLabs) talking about their latest challenges and solutions in the area of recommender systems.
- The meetup will start at 5:30PM sharp.
- Address: Hoogoorddreef 60, 1101 BE Amsterdam
- Please note that you need to sign up for a free ticket to participate. Tickets are available at our Eventbrite page.
Talk 1: Recommendation systems for jobs and people (by Jakub Zavrel)
Most systems in HR, recruitment and job search are very transactional because the software does not understand the concepts described in unstructured documents: CVs and job profiles. This transactional nature makes it very difficult to optimize conversion for both the job seeker and the recruiter. Semantic technology, and in particular semantic matching of jobs and people has the potential to significantly improve the user experience and effectiveness for both sides in the labor market. The key enabling technology is NLP-based understanding of unstructured data and the application of domain knowledge to facilitate more relevant recommendations. In this talk I will sketch the most promising use cases, current technology limitations, give an insight into ongoing R&D by Textkernel, and present a number of challenges for the audience.
Jakub Zavrel is founder and CEO of Textkernel, a semantic recruitment technology company based in Amsterdam. With an R&D background in natural language processing, big data, machine learning, and semantic search, he and his team work on building technology for the future of global recruiting. For multi-lingual signal and noise in the latest semantic technologies. You can follow him on twitter @jakubzavrel
Talk 2: Recommending Job Ads to People (By Fabian Abel)
XING is a business network that allows people to find new jobs and other business & career opportunities. In this talk, we will sketch the development of the job recommendation system that serves Millions of XING users with job offers. We will discuss key challenges of building such a recommender system, present key features of the job recommendation algorithms and outline the technical infrastructure on which we operate our recommender systems.
Fabian is a Data Scientist at XING and enjoys working on large-scale data mining problems and delivering data products to large audiences. You can follow him on twitter @fabianabel_
Talk 3: Do’s and not to do’s while building an Industrial Recommendation System (By Rahim Delaviz)
In this presentation Rahim Delaviz will share his experience on building a Recommendation System for an online retailer, CoolBlue. He will cover different aspects of a Recommendation System in an industrial environment, like what should be the focus of the team, how to tackle such a project in smaller steps, how to pick the right tools, techniques and data. Finally, what are the best practices to evaluate a Recommendation System?
Rahim Delaviz was born and grow up in the East-Azerbaijan province of Iran, located on the NW part of the country. He did his BSc. in Software Engineering and MSc. in Mathematical programming at SUT, Tehran-Iran. After a few years of working at Iran, he moved to Sweden and followed a MSc. in Distributed Systems at KTH, Stockholm. In 2009 he started his PhD at TU Delft, Netherlands, with the thesis focused on Distributed Reputation Mechanisms. After graduation in 2013, he joined CoolBlue as a Data Scientist, and since then he has been involved on a number of projects such as RS and Big Data Analytics.
Talk 4: Using users for online evaluation and learning (by Anne Schuth)
Deployed information systems are constantly changing. Collections change, users preferences change but above all, researchers and engineers constantly develop potential improvements to algorithms. It is vital for such systems to constantly measure their performance. In particular because by far most of the potential improvements are likely to actually degrade system performance. Even if they seem to be obviously good ideas. The methodology for measuring system performance has radically changed recently and in industry is now mostly done by observing and interpreting user interactions with the system. In this talk, I will stress the importance of online experimentation and I will discuss several online evaluation approaches. Finally, I will touch on approaches that use these online evaluation methods to automatically improve information systems.
Anne Schuth is a third year PhD candidate at the University of Amsterdam within the Information and Language Processing Systems. Earlier this year, he was research intern for 3 months at Yandex and then for 3 more months at Microsoft Research in Cambridge. His current research focuses on interpreting user interactions with search engines. He is interested in how these interactions can be used for evaluation as well as learning.