PhD Dissertation Defense by ZHANG Hao | Recommender system design and multi-channel pricing: Personalization strategies for online platforms

ABOUT THE TALK

The advancement of mobile technology and rising consumer demand have contributed to the unprecedented growth of online platforms. Entertainment platforms host a vast amount of user-generated content (UGC). The unique feature of UGC entertainment platforms is that creators’ content generation and users’ content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address this challenge, we propose a new framework to balance content generation and usage through personalized content recommendation and display decisions. In addition, an increasing number of e-commerce platforms introduce multiple sales channels, which facilitate consumers to search products across multiple channels and leave their footprints. Optimizing multiple-channel prices based on consumers’ footprints is vital and challenging for these platforms. Thus, we design an innovative pricing method based on consumers’ multi-channel footprints. In conclusion, this thesis designs novel multistakeholder recommendation and multi-channel pricing strategies for online platforms.

ABOUT THE SPEAKER

ZHANG Hao is a Ph.D. candidate at the School of Computing and Information Systems, Singapore Management University. He conducts research on information systems under the supervision of Prof. MA Dan and Prof. GUO Zhiling. He received the Best Student Paper Award from the International Conference on Information Systems (ICIS) in December 2022. Before joining SMU, he graduated with a Bachelor’s degree in Information Management and Information Systems from Central South University in China.