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Activity Number: 485 - Decision Making in Tech Giants Through A/B Testing, Prediction and Optimization
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Quality and Productivity Section
Abstract #300429
Title: A Multi-Objective Optimization for Web Based Ranking Problems
Author(s): Souvik Ghosh*
Companies: LinkedIn Corporation
Keywords: Recommender systems; multi-objective optimization

Web-based ranking problems involve ordering different kinds of items in a list or grid to be displayed in mediums like a website or a mobile app. In most cases, there are multiple objectives or business metrics that we want to balance and constructing a serving algorithm that achieves the desired tradeoff among multiple objectives is challenging. In addition, it is often not possible to estimate such a serving scheme using offline data alone for non-stationary systems with frequent online interventions.

We consider a large-scale online application where metrics for multiple objectives are continuously available and can be controlled in a desired fashion by changing certain control parameters. We assume that the desired balance of metrics is known from business considerations. Our approach models the balance criteria as a composite utility function via a Gaussian process over the space of control parameters. We show that obtaining a solution can be equated to finding the maximum of the Gaussian process. Implementing such a scheme for large-scale applications is challenging. We provide a novel framework to do so and illustrate its efficacy in the context of LinkedIn Feed.

Authors who are presenting talks have a * after their name.

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