Lessons Learned from Building Real-Life Recommender Systems
by Xavier Amatriain (Quora, USA) and Deepak Agarwal (LinkedIn, USA)
In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community, it put the focus on the wrong approach and metric while leaving many important factors out.
In this talk we will talk we will describe the advances in Recommender Systems in the last 10 years from an industry perspective based on the instructors’ personal experience at companies like Quora, LinkedIn, Netflix, or Yahoo!. We will do so in the form of different lessons learned through the years. Some of those lessons will describe the different components of modern recommender systems such as: personalized ranking, similarity, explanations, context awareness, or multiarmed bandits. Others will also review the usage of novel algorithmic approaches such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or List-wise learning to rank. Others will dive into details of the importance of gathering the right data or using the correct optimization metric.
But, most importantly, we will give many examples of prototypical industrial scale recommender systems with special focus on those unsolved challenges that should define the future of the recommender systems area.
Sunday, Sept 18, 2016, 16:20-18:00
Stratton Student Center (Sala 202)