Katta Murty Best Paper Prize awarded to Junyu Cao

The Katta G. Murty Best Paper Prize is awarded to Junyu Cao for her outstanding paper “Sequential Choice Bandits: Learning with Marketing Fatigue.” The Katta G. Murty Prize, established in 2006 as a gift from IEOR alum Katta Murty (’68 PhD IEOR), is an annual competition for graduate students in the IEOR Department for exceptional papers focused on optimization.

The abstract from “Sequential Choice Bandits: Learning with Marketing Fatigue” and citation can be found below. To read more, click here.

Junyu Cao has won the Katta Murty Best Paper Prize.
She also recently received the 2019 IBM PhD Fellowship

Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messages to its users and is penalized when users abandon the platform due to marketing fatigue. We propose a novel sequential choice model to capture multiple interactions taking place between the platform and its user: Upon receiving a message, a user decides on one of the three actions: accept the message, skip and receive the next message, or abandon the platform. Based on user feedback, the platform dynamically learns users’ abandonment distribution and their valuations of messages to determine the length of the sequence and the order of the messages, while maximizing the cumulative payoff over a horizon of length T. We refer to this online learning task as the sequential choice bandit problem. For the offline combinatorial optimization problem, we show that an efficient polynomial-time algorithm exists. For the online problem, we propose an algorithm that balances exploration and exploitation, and characterize its regret bound. Lastly, we demonstrate how to extend the model with user contexts to incorporate personalization.

Cao, Junyu & Sun, Wei & Max Shen, Zuo-Jun. (2019). Sequential Choice Bandits: Learning with Marketing Fatigue. SSRN Electronic Journal. 10.2139/ssrn.3355211.