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April 15-18, 2019
New York, NY
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A Reinforcement Learning Approach to Optimizing Preference on a Social Network

Matthew Reyes (Independent Researcher and Consultant)
4:55pm5:35pm Thursday, April 18, 2019
Models and Methods
Location: Rendezvous
Secondary topics:  Media, Marketing, Advertising, Models and Methods, Reinforcement Learning

Who is this presentation for?

Data Scientists, Marketers, Executives (quantitative experience and / or experience with high-level resource allocation decisions)



Prerequisite knowledge

Attendees should be familiar with: 1. Monte Carlo simulation 2. graphical representation of a social network 3. estimating parameters from data 4. notion of utility and choice models

What you'll learn

Attendees will learn about a straightforward perspective on how to view the problem of optimizing preference on a social network.


The problem of influencing preference towards products on social networks has attracted considerable attention over the past couple of decades. Previous approaches have suffered two subtle yet significant drawbacks. The first is that they model consumer decision-making as best-response, deterministic maximization of some numerical utility. The second is that their decomposition of utility does not include influence by marketers for the respective companies.

In this talk we cast consumer decision-making within the framework of random utility. Random utility theory views so-called utility as a parametrization of observed frequencies of choice. The decomposition of utility will correspond to variables that are either observable through data collection or under the control of an external agent, in this case a company.

The decomposition of utility that we present explicitly includes influence by marketers from two competing companies. Incorporating the marketer into the model of consumer decision-making allows a company to evaluate the effect of different marketing allocations on the evolution of preferences on the network.

The combination of a random choice model and the inclusion of marketers into the model allow this important problem to be cast in the reinforcement learning paradigm. In this talk we present a simplified scenario illustrating the steps in a company’s allocation decision, from learning parameters from data, to evaluating the consequences of different marketing allocations.

Photo of Matthew Reyes

Matthew Reyes

Independent Researcher and Consultant

B.S. and M.S. in math at Wichita State University, Wichita, KS

M.S. and Ph.D. in ee:systems at University of Michigan, Ann Arbor, MI

worked 4+ years at MIT Lincoln Laboratory

currently doing freelance work and developing a reinforcement learning based approach to influence maximization on social networks

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