The first RecSys Meetup in Amsterdam will be hosted by CWI on August 19th at 18:00.
We will start at around 18:00 at CWI where there’ll be three talks, refreshments and snacks. After the talks we head over to Cafe Restaurant Polder to mingle and have some drinks.
Please note that participants need to sign up for tickets at our Eventbrite page
We’re very excited to announce the following talks:
Talk 1: Recommendations at Booking.com: All RecSys Are Not Created Equal – Lukas Vermeer @lukasvermeer
Booking.com is the world’s leading online hotel and accommodation reservations company, and guarantees the best prices for any type of property – from small independents to five-star luxury. Guests can access the Booking.com website anytime, anywhere from their desktop, mobile phone or tablet device, even without an account. The Booking.com website is available in 41 languages, offers over 330,000 hotels and other accommodation properties in 180 countries.
The scale and diversity of the Booking.com product and customer base poses quite a challenge to our recommender systems applications. Additionally, there are obstacles such as sparsity, scarcity, anonymity and the effects of time and price that are more relevant in industries where product prices are generally significantly greater than that of a rental movie or a book.
In this talk, we will look at some of the different applications of recommender systems as they appear on the Booking.com site, dive into specific challenges we face when designing recommendation systems for the hospitality industry and discuss the necessity of mixing types of recommender systems with business logic to find the right balance.
Lukas Vermeer is a Data Scientist at Booking.com
WTF (“Who to Follow”) is Twitter’s user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which significantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of
our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter’s user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.
Jimmy Lin is an associate professor in the iSchool at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies. Recently, Lin just completed an extended sabbatical at Twitter, where from 2010-2012 he worked on services designed to surface relevant content for users and the distributed infrastructure that supports mining relevance signals from massive amounts of data.
Talk 3: Scalable search suggestions with Hadoop/MapReduce (provisional) - Niels Basjes @nielsbasjes
In this talk, we will look at scalability of search suggestions (item-to -item recommendations of the most probable search term) in an online shopping scenario using Hadoop/MapReduce.
Niels Basjes has been working for bol.com since May 2008. Before that he was working as a Web analytics architect for Moniforce, and as an ICT architect/researcher at the National Aerospace Laboratory in Amsterdam. Since the second half of the 1990s he has been working on processing problems that require scalability. He has applied these concepts in the past 15 years in aircraft/runway planning, IT operations and in the field of web analytics to build reports for some of the biggest websites in the Netherlands. Also at bol.com the primary focus of Niels Basjes are scalability problems and he is responsible for a shift in thinking about data and the business value it contains. Niels studied Computer Science at the TU Delft, and has Business administration degree at Nyenrode University.