Personalized recommendation and balancing content generation and content usage on two-sided entertainment platforms
Publication Type
Conference Proceeding Article
Publication Date
12-2022
Abstract
Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided 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 the challenge, this paper proposes a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Specifically, we first predict the heterogeneous treatment effects (HTEs) of recommended contents on creators’ content generation and users’ content usage, then consider these two predicted HTEs simultaneously in an optimization model to determine the recommended contents for each user. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a 42% increase in participants’ activity compared to existing benchmark methods.
Keywords
Recommender systems, prescriptive analytics, user-generated content, content generation and usage
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization; Data Science and Engineering; Information Systems and Management
Publication
Proceedings of the 43rd International Conference on Information Systems (ICIS)
City or Country
Copenhagen, Denmark
Citation
ZHANG, Hao; GUO, Zhiling; and WANG, Mingzheng.
Personalized recommendation and balancing content generation and content usage on two-sided entertainment platforms. (2022). Proceedings of the 43rd International Conference on Information Systems (ICIS).
Available at: https://ink.library.smu.edu.sg/sis_research/7726