Explore the latest research from Facebook

We propose a theoretical framework for studying such amplification in a matrix factorization based recommender system. We model the dynamics of the system, where users interact with the recommender systems and gradually “drift” toward the recommended content, with the recommender system adapting, based on user feedback, to the updated preferences.

This paper describes a scalable solution to deploy JupyterHub for computational social science research on the cloud. We use a reference architecture on AWS to walk through the design principles and details.

We show that it improves on the existing approaches that use the Cramer-Chernoff technique such as the Hoeffding, Bernstein, and Bennett inequalities. The resulting confidence sequence is confirmed to be favorable in synthetic coverage problems, adaptive stopping algorithms, and multi-armed bandit problems.

In this talk, we discuss how these systems will need to evolve from the traditional formulations by incorporating the producer value into their objectives. Jointly optimizing the ranking functions behind these systems on both consumer and producer values is a new direction and raises many technical challenges.

We use anonymized and aggregated data from Facebook to explore the spatial structure of social networks in the New York metro area. We find that a substantial share of urban residents’ connections are to individuals who are located nearby.

Motivated by a mechanism used in practice by several companies, this paper considers a smoothing procedure that relies on pacing multipliers: on behalf of each buyer, the auction market applies a factor between 0 and 1 that uniformly scales the bids across all auctions.

In this paper, we present an at-scale, near-realtime reboot monitoring framework built with multiple state-of-the-art data infrastructures, as well as machine learning-based anomaly detection and automated root cause analysis across hundreds of server attribute combinations.

In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in a framework we call Lightweight Inference Compilation (LIC). LIC implements amortized inference within an open-universe declarative probabilistic programming language (PPL).

We focus on Internet advertising auctions, fair division problems, content recommendation systems, and robust abstractions of large-scale markets.

Although there have been numerous studies about underrepresentation and misrepresentation of people in advertising, most have focused on traditional channels such as television, print, and radio, rather than on digital channels. In this paper, we seek to contribute to the body of knowledge by utilizing a mix of quantitative and qualitative methods to explore people’s attitudes toward diversity in online advertising, the current state of representation, and the impact of diversity on digital campaign performance.