The 4th RecSys NL meetup will be on Tuesday May 19th at TNO Leiden. The meetup starts at 17:30 followed by drinks and pizza. The address is: Gaubiusgebouw, Zernikedreef 9, 2333 CK Leiden.
Please note that you need to sign up for a free ticket to participate. Tickets are available at our Eventbrite page.
There’s an awesome lineup of four talks:
Talk 1: “Recommendation technology for media services.” by Martin Prins from TNO
The media industry is under pressure. There’s lot’s of competition, causing users to move to other services or to use services less frequently. This is a major concern for many media companies, as they typically rely on advertising revenue. TNO has recently been involved in research projects with several Dutch media companies, in which recommendation technology plays an important role.
In this talk Martin will present several case studies and shed insights into the role of recommendations for media services, the technical challenges that were faced, and findings of the research projects conducted by TNO.
Martin Prins is a Technical Consultant at TNO within the Media and Network Services group. With an R&D background in multimedia, especially streaming media and broadcast TV, Martin’s current focus is helping broadcasting, news and media companies with their digital media innovations, in which personalization and recommendations play an ever more important role.
Martin holds a Master Degree in Telematics from Twente University.
Talk 2: “Recommend Now, Not in the Past: Leveraging Contextual User Profiles for Destination Recommendations” by Melanie Mueller & Lucas Bernardi from Booking.com
At Booking.com, we recommend destinations to travelers who are not yet sure where to go. Typical recommender systems rely on past user feedback to recommend items to users. This can be problematic in our real-world application, where user interactions are infrequent (sparse data), and where many users come in for the first time or change interest over time (continuous cold start problem). Here, we propose to use the current user situational context, instead of past user interactions, to inform recommendations. In an A/B test on Booking.com users, contextual recommendations increased user engagement by 20%.
Melanie and Lucas are data scientists at Booking.com, where they enjoy transforming terabytes of data into recommendations for fun holidays.
Talk 3: “Large-Scale Real-Time Product Recommendation At Criteo” by Romain Lerallut & Olivier Koch from Criteo
Behavioral retargeting consists of displaying online advertisements that are personalized according to each user’s browsing history. As it is performed for each personalized banner, billions of times a day, the selection of the products to display in the banner needs to be fast and accurate.
At Criteo, we built a recommender system which is able to choose a dozen of relevant products from over two billion products in a few milliseconds. In this talk, we will expose the problems we faced whilst building this system and how we solved them thanks to a mix of online and offline computations.
Romain Lerallut is a Senior Engineering Manager at Criteo in the Engine team, in charge of applying large-scale machine learning algorithms to actual problems such as product recommendation or graphical layout optimization. Romain has been working on building up the Engine since 2011, first as devlead and then as manager. He has an engineering degree from “Ecole des Ponts-Paristech” and a PhD in Computer Science from “Ecoles des Mines-Paristech”.
Olivier Koch is an Engineering Program Manager at Criteo, with a specific focus on prediction algorithms and recommender systems. Prior to joining Criteo, Olivier worked as a software engineer and team manager in the fields of image processing at General Electric Healthcare and Thales Optronics. Olivier holds an engineering degree from ENSTA-Paristech and a PhD degree in Computer Science from Massachusetts Institute of Technology.
Talk 4: “Musical similarity and recommendations for DJ apps” by Victor Bergen Henegouwen from Elephantcandy
Music is a difficult matter to analyse; unlike text, there is no unambiguous language and notation. Transcription, notation or annotation of music typically describes a very limited set of features and is highly decoupled from the actual audio.
In Electronic Dance Music (EDM), this problem is even more pronounced; notation rarely exists and common structural elements like choruses and couplets are absent. Instead, the most distinct features of a dance track are the ‘sound’ and (very) subtle variations in rhythm. Together with the ILLC department of the UvA, Elephantcandy has been working on a system to extract features from the audio that are relevant in EDM, make them comparable and recommend parts of songs that can be mixed well together.
Niels Bogaards is co-founder and CTO of Elephantcandy, an Amsterdam-based app development company focussed exclusively on mobile audio. Previously he worked at IRCAM in Paris as researcher/developer in the Analysis-Synthesis Team.