I am Hong Deng, a 4-th year PhD candidate in Marketing at the Department of Business Economics, Erasmus School of Economics. My research interests lie in marketing models and marketing analytics.
My research focuses on methodological advancements in the domain of personalization, which is a challenging but increasingly important strategy in today's fast-paced online marketplace. I propose new personalization algorithms to tackle real-world challenges, such as real-time implementation, introduction of new personalized offers, and high-dimensional features. Before my PhD trajectory, I studied Economics at Tinbergen Institute.
Research interests
My research interests are personalization, recommendation systems, digital marketing, and marketing analytics.
Real-Time Personalization in Dynamic Environments
Real-time personalization engines can enable effective customization in E-commerce by finding the optimal offer to provide to specific customers. Yet, the development of such engines is not trivial. It remains challenging to optimize an offer strategy in real time, especially in a dynamic environment where the set of available offers varies over time. The complexity is further enhanced when trying to utilize situational information on top of customer characteristics. We provide an easy-to-implement personalization engine to quickly learn, and serve, optimal context-dependent offers in a situation where the offer set may change over time. We formalize this personalization problem in the multi-armed bandit framework, and propose a new contextual bandit algorithm boosted by the particle filtering estimation technique. Our method allows firms to flexibly introduce new personalized offers, calibrate their anticipated performance using prior knowledge from historical data and rapidly update these prior beliefs as new information arrives. We show in a news article recommendation setting that, relative to state-of-the-art competing methods, the proposed method improves the click-through-rate by 3.7-6.5% and gains computational efficiency by saving 80% of required computing resources.
CV
My CV is available on Google drive
Contact
References
Prof. Bas Donkers (Co-Advisor)
Professor of Marketing Research
Department of Business Economics
Erasmus School of Economics
Erasmus University Rotterdam
Web: https://www.erim.eur.nl/people/bas-donkers/
Tel: +31 10 4082411
Email: donkers@ese.eur.nl
Prof. Dennis Fok (Co-Advisor)
Professor of Econometrics and Data Science
Department of Econometrics
Erasmus School of Economics
Erasmus University Rotterdam
Web: https://personal.eur.nl/dfok/
Tel: +31 10 4081333
Email: dfok@ese.eur.nl
Dr. Vardan Avagyan
Assistant Professor of Marketing
Department of Business Economics
Erasmus School of Economics
Erasmus University Rotterdam
Tel: +31 10 4082542
Email: avagyan@ese.eur.nl