10th Recommender systems Amsterdam meetup

We look forward to welcoming you to the 10th RecSys Amsterdam meetup. Hosted by Persgroep with two interesting talks from industry and academia.

Doors open at 18:00h, talks start at 18:30h.
Drinks and pizza will be provided.

1. Daan Odijk, RTL, "Searching to be Entertained"

As the largest commercial broadcaster in a declining Dutch TV market, RTL is making a transition from a traditional TV company to a consumer-focused media company. RTL is embracing a closer relationship and more direct interaction with its viewers, followers and visitors. In this talk, I will share how we are using AI, recommender systems and search technology to help our users find the right content for them, ranging from the 1M daily visitors on our news website to the over 2B video plays we had in 2017, most of these on our rapidly growing video-on-demand platform Videoland.

Bio: Daan Odijk is the lead data scientist at RTL. In 2016, he obtained his PhD from the University of Amsterdam, researching search algorithms for news. Subsequently, he joined journalism start-up Blendle to lead the personalization team. At RTL since 2018, Daan leads a team of a dozen data scientists and engineers, delivering data-powered products across RTL, including personalization for RTL Nieuws and Videoland.

2. Julián Urbano, TU Delft. "The measure dilemma: which dataset-based measures are better to predict end-user satisfaction?"

The main goal of an evaluation experiment is to determine which systems perform well and which systems perform poorly on a task like retrieval or recommendation. However, there has been little systematic analysis regarding how well these evaluation results predict end-user satisfaction. For many researchers, reaching statistical significance is usually the objective, but not enough attention is paid to the real implications of the observed improvements. In this talk I'll present empirical results on the correlation between dataset-based results and user satisfaction in the task of music retrieval for recommendation. In particular, we'll discuss the effect that different annotation scales and measure formulations have on this correlation, and the implications for researchers, developers or reviewers.

Bio: Julián Urbano is an Assistant Professor at Delft University of Technology. His research is primarily concerned with evaluation in IR, working in both the music and text domains. Current topics of interest are the application of statistical methods for the construction of datasets, the reliability of evaluation experiments, statistical significance testing for IR, low-cost evaluation and stochastic simulation for evaluation. He has published over 50 research papers in related venues like Foundations and Trends in IR, the IR Journal, the Journal of Multimedia IR, SIGIR, ISMIR, CIKM, ICTIR and ECIR, winning two best paper awards and a best reviewer award.