Industry Session 1: Core Algorithms

Date: Wednesday, Oct 3, 2018, 14:00-15:30
Location: Parq D/E/F
Chairs: Alexandros Karatzoglou, Ben Frederickson

Variational Learning to Rank (VL2R)

by Keld Lundgaard (SalesForce)

We present Variational Learning to Rank (VL2R), a combination of variational inference and learning to rank. The combination provides a natural way to balance exploration and exploitation of the algorithm by introducing shuffling of product search/category listings according to the model’s relevance uncertainty for each product. Simply put, we perturb (newer) products with higher uncertainty on the relevance more than (older) products which have a lower uncertainty on the relevance.
Our formalism makes it possible to train an end-to-end model that optimizes for both ranking and shuffling, compared to known state-of-the-art systems where ranking and shuffling are treated as separate problems. VL2R provides an integrated way of doing propensity scoring during the offline learning phase, thus reducing selection bias. The system is simple, yet powerful and flexible. We have implemented it within the Salesforce Commerce Cloud; a platform 500 million unique online shoppers interact with each month across 2,750 websites in 53+ countries as of FY18.
In this talk, we will go into the details of our variational learning to rank system and share our early experiences with optimizing VL2R and running it in production. We hope that by sharing VL2R with the recommendation systems community, we will foster more research in this direction, and result in systems that are faster at learning user preferences for changing catalogs.

About the Speaker

Keld Lundgaard is a senior data scientist at Salesforce Commerce Cloud Einstein. He has developed and implemented on a number of recommendation systems that are currently served across Commerce Cloud websites. Prior to Salesforce, Keld was a postdoctoral fellow at Stanford University, where he developed machine learning models to improve the accuracy of surface science simulations used for screening new material compounds for batteries, fuel cells, and artificial photosynthesis. Keld holds a Ph.D. from Technical University of Denmark.

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