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

The advancement of mobile technology and rising consumer demand have contributed to the unprecedented growth of online platforms. Entertainment platforms such as YouTube and Spotify 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. In addition, an increasing number of e-commerce platforms introduce multiple sales channels. Optimizing multiple-channel prices is vital and challenging for these platforms. Thus, this thesis aims to design multistakeholder recommender systems and multi-channel pricing strategies for online platforms.

ZHANG Hao is a Ph.D. candidate at the School of Computing and Information Systems, Singapore Management University, supervised by Professor GUO Zhiling. His research interests focus on data-driven decision-making.