Designing Sustainable Recommender Systems

Recommender systems are widely deployed to serve users with content they like. However, content must be created and insufficient demand dampens a creator’s production incentive. We argue that the canonical recommender system may not be sustainable if, by promoting the content each user likes the most, it suppresses the creation incentive of the less popular but still valuable content. We propose a “sustainable recommender system” solution – subsidize creators with demand according to their “sensitivity,” which measures how easily a creator can be incentivized by demand, and their “contribution,” which measures how important a creator is to users overall. Theoretically, we prove that this algorithm maximizes long-term user utility by internalizing the externality of user choice on other users. Computationally, our main innovation is to estimate creator contribution using computer vision, where we train a deep-learning model to compute how creator distribution affects system-wide user utility. Analyzing data from a large content platform, we show that our algorithm incentivizes valuable creators and sustains long-term user experience.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Huang, Lei

Advisor dc:contributor.advisor

• Zhang, Juanjuan

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/158881

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/158881