Past Meetup

9th Recommender Systems Amsterdam meetup

This Meetup is past

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Details

We look forward to welcoming you to another RecSys Amsterdam meetup! Kindly hosted by Booking.com, we'll have three speakers in a mix from industry & academia. Doors open at 18:00, talks start at 18:30!
As usual, drinks and snacks will be provided.

Lineup:

1. Christophe van Gysel (UvA) will talk about work he's done at Bing: "Reply With: Proactive Recommendation of Email Attachments" (see: https://arxiv.org/abs/1710.06061)

2. Dung Chu (FD Mediagroep): "Recommendation systems at the FD"
Recommendation system is a popular data-driven tool used in many different business sectors. Within het FD we use an in-house recommendation engine in personalized email campaigns. In this talk, I will share our experience in: why we decided to develop our own recommendation engine; challenges that we encountered from business and technological points of view; first A/B testing results; and our future plan.

3. Themis Mavridis (Booking.com): "Learning To Match"
Booking.com is a virtual two-sided marketplace where guests and accommodation providers are the two distinct stakeholders. They meet to satisfy their respective and different goals. Guests want to be able to choose accommodations from a huge and diverse inventory, fast and reliably within their requirements and constraints. Accommodation providers desire to reach a reliable and large market that maximises their revenue. Finding the best accommodation for the guests, a problem typically addressed by the recommender systems community, and finding the best audience for the accommodation providers, are key pieces of a good platform. This work describes how Booking.com extends such approach, enabling the guests themselves to find the best accommodation by helping them to discover their needs and restrictions, what the market can actually offer, reinforcing good decisions, discouraging bad ones, etc. turning the platform into a decision process advisor, as opposed to a decision maker. Booking.com implements this idea with hundreds of Machine Learned Models, all of them validated through rigorous Randomised Controlled Experiments. We further elaborate on model types, techniques, methodological issues and challenges that we have faced. (see: https://export.arxiv.org/pdf/1802.03102)