The document is under construction now. Some information may be missing.
RecNet
RecNet is a human-driven recommendation system for academic readings. RecNet implements a mechanism similar to contemporary social networks, but it is designed to be impoverished in certain ways through information bottlenecks that increase communication cost. This is intended to limit the amount of time the system consumes from its users, while increasing the quality of information passed. The RecNet mechanism was initially outlined by Yoav Artzi in a Substack (opens in a new tab) post. RecNet is currently in initial development stages.
The team
The development of RecNet project is led by Yoav Artzi (opens in a new tab) and maintained by Frank Hsu and Joanne Chen at Cornell Tech. Feel free to reach out to us if you have any questions or want to contribute.