Taking Recommendation to the Masses

Recent decades have witnessed a great proliferation of recommendation systems. The technology has brought significant benefits to many business verticals. From earlier algorithms such as similarity-based collaborative filtering to the latest deep neural network-based methods, recommendation technologies have evolved dramatically. To a certain extent, this makes it challenging for practitioners to select and customize the optimal algorithms for a specific business scenario. In addition, operations such as data pre-processing, model evaluation and system operationalization play a significant role in the lifecycle of developing a recommendation system; however, they are often neglected by practitioners.

Based on extensive experience in productization of recommendation systems in a variety of real-world application domains, this talk will review and demonstrate the main key tasks in building recommendation systems. It will present best practices and provide examples of democratizing recommendation systems for every organization and the wider community.

Open source GitHub repository Microsoft/Recommenders (https://github.com/Microsoft/Recommenders) will be used for the hands-on practice. This repository, where the key topics are shared as Jupyter notebooks and a utility function codebase, is designed to help data scientists quickly grasp basic concepts in a hands-on fashion. It has gained good visibility within the community, with more than 3,600 stars on GitHub. The best practice examples shared in the repository are meant to help developers, scientists or researchers to quickly build production-ready recommendation systems as well as to prototype novel ideas using the provided utility functions. The talk will walk through several recommendation algorithms in order to provide an in-depth understanding of the available techniques.