We are just a few days away from the 2012 ACM Recommender Systems Conference (#Recsys2012), that this year will take place in Dublin, Ireland. Over the years, Recsys has been and still is one of our favorite conferences, not only because of how relevant the area is to our business, but also because of its unique blend of academic research and industrial applications.
In fact, if you had to mention a single company that is identified with recommender systems and technologies, that would probably be Netflix. The Netflix Prize started a year before the first RecSys conference in Minneapolis, and it impacted Recommender Systems researchers and practitioners in many ways. So, it comes as no surprise that the relation between the conference and Netflix also goes a long way. Netflix has been involved in the conference throughout the years. And, this time in Dublin is not going to be any different. Not only is Netflix a proud sponsor of the conference, but you will also have the chance to listen to presentations and meet some of the people that make the wheels of the Netflix recommendations turn. Here are some of the highlights of Netflix' participation:
- Harald Steck and Xavier Amatriain are involved in organizing the workshop on "Recommender Utility Evaluation: Beyond RMSE". We believe that finding the right evaluation metrics is one of the key issues for recommender systems. This workshop will be a great event to not only discover the latest research in the area, but also to brainstorm and discuss on the issue of recsys evaluation.
- On that same workshop, you should not miss the keynote by our Director of Innovation Carlos Gomez-Uribe. The talk is entitled "Challenges and Limitations in the Offline and Online Evaluation of Recommender Systems: A Netflix Case Study". Carlos will give some insights into how we deal with online A/B and offline experimental metrics.
- On Tuesday, Xavier Amatriain will be giving a 90 minute tutorial on "Building industrial-scale real-world Recommender Systems". In this tutorial, he will talk about all those things that matter in a recommender system, and are usually outside of the academic focus. He will describe different ways that recommendations can be presented to the users, evaluation through A/B testing, data, and software architectures.