Publication

Searching for Communities: a Facebook Way

ACM SIGIR Conference on Research and Development in Information Retrieval


Abstract

Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems. First, there is a vocabulary mismatch problem since most of the content of the communities is private. Second, the common labeling strategies based on human ratings and clicks do not work well due to limited public content available to third-party raters and users at search time. Finally, community search has a dual objective of satisfying searchers and growing the number of active communities. While A/B testing is a well known approach for assessing the former, it is an open question on how to measure progress on the latter. This talk discusses these challenges in depth and describes our solution.

Related Publications

All Publications

ICLR - May 4, 2021

Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin Benson

Security and Safety in Machine Learning Systems Workshop at ICLR - May 7, 2021

Ditto: Fair and Robust Federated Learning Through Personalization

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

ICLR - May 3, 2021

Creative Sketch Generation

Songwei Ge, Vedanuj Goswami, Larry Zitnick, Devi Parikh

ICML - December 1, 2020

Certified Data Removal from Machine Learning Models

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy