Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Harnessing and customizing state-of-the-art recommendation solutions with OpenRec

Longqi Yang (Cornell Tech, Cornell University)
1:15pm–1:55pm Wednesday, 09/12/2018
Data science and machine learning
 Location: 1A 15/16 Level: Intermediate
Secondary topics:  Deep Learning, Media, Marketing, Advertising, Recommendation Systems, Retail and e-commerce

Who is this presentation for?

  • Software and data engineers, data scientists, and machine learning researchers

Prerequisite knowledge

  • A basic understanding of machine learning and statistics

What you'll learn

  • Explore state-of-the-art for recommendation algorithms, including deep learning-based approaches
  • Understand modular design for recommendation algorithms and how to harness and customize recommendation solutions using OpenRec

Description

Today’s recommendation systems ingest a wide range of information, such as diverse user feedback signals (ratings, clickthrough, likes, and views) and auxiliary, contextual, and cross-platform traces (images, video, audio, and other associated metadata, as well as social networks and personal digital traces). A state-of-the-art system usually involves numerous heterogeneous and complex submodels that analyze and fuse high-dimensional and multichannel data streams.

Current development practice usually treats a recommendation algorithm as singular and monolithic. As a result, in order to experiment with a new method for even a small part of an algorithm or customize an algorithm for other application scenarios, researchers and practitioners need to reimplement the whole model from scratch or extensively patch existing code.

OpenRec, an open source framework that modularizes recommendation algorithms, was designed to tackle these challenges. Each recommender is modeled as a structured ensemble of reusable modules with standard interfaces. Under such a paradigm, changes to a module or the computational graph do not affect other components, and development and testing can be more readily achieved via plug-ins.

Longqi Yang explains how to use OpenRec to easily customize state-of-the-art solutions for diverse scenarios.

Topics include:

  • A general introduction to recommendation algorithms
  • State-of-the-art solutions, including deep learning-based approaches
  • OpenRec library: Modularizing recommendation algorithms
  • Use case on model selection and adaptation
  • Use case on developing new recommendation algorithms
Photo of Longqi Yang

Longqi Yang

Cornell Tech, Cornell University

Longqi Yang is a PhD candidate in computer science at Cornell Tech and Cornell University, where he is advised by Deborah Estrin, and is a member of the Connected Experiences Lab and the Small Data Lab. His current research focuses are user modeling, recommendation systems, and recommendation for social good. His work has been published and presented in top academic conferences, such as WWW, WSDM, Recsys, and CIKM. He co-organized workshops at the NYC Media Lab annual summit 2017 and KDD 2018.