Abstract: 

Recent decades have witnessed a great proliferation of recommendation systems, which have found application in many business verticals. It is however challenging for practitioners to select and customize the optimal algorithms for a specific business scenario. In addition to training and tuning the appropriate algorithms, a complete recommendation system also consists of operations such as data pre-processing, model training, model evaluation and system operationalization. 
Motivated by our extensive experience in productization of recommendation systems in a variety of real-world application domains, in this talk, we will review complete pipelines of building recommendation systems. We will start by introducing some standard factorization machine algorithms. Thereafter, we will address some of the latest advances in deep learning algorithms in the area, with an emphasis on knowledge graph models. Then we will analyse different methodologies for computing these algorithms at scale, reviewing some available techniques for hyperparameter tuning. Finally, we will discuss how these systems can be brought successfully into production.
To support this talk, an extensive suite of algorithms, utilities and Jupyter notebooks are open source and publicly available in our Recommenders repository (https://github.com/Microsoft/Recommenders).

Bio: 

Miguel González-Fierro is a Sr. Data Scientist at Microsoft UK, where his job consists of helping customers leverage their processes using Big Data and Machine Learning. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, small humanoids competitions, and 3D printers. Miguel also worked as a robotics scientist at Universidad Carlos III of Madrid and King’s College London, where his research focused on learning from demonstration, reinforcement learning, computer vision, and dynamic control of humanoid robots. He holds a BSc and MSc in Electrical Engineering and a MSc and PhD in robotics.