118 Results
June 13, 2019
Discovering Context Effects from Raw Choice Data
International Conference on Machine Learning (ICML)
Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by “irrelevant” aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data.
By: Arjun Seshadri, Alexander Peysakhovich, Johan Ugander
Data Science 
Economics & Computation 
Facebook AI Research June 1, 2019
Top Challenges from the first Practical Online Controlled Experiments Summit
SIGKDD Explorations
To understand the top practical challenges in running OCEs at scale, representatives with experience in large-scale experimentation from thirteen different organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, Yandex, and Stanford University) were invited to the first Practical Online Controlled Experiments Summit. All thirteen organizations sent representatives.
By: Somit Gupta, Ronny Kohavi, Diane Tang, Ya Xu, Reid Andersen, Eytan Bakshy, Niall Cardin, Sumitha Chandran, Nanyu Chen, Dominic Coey, Mike Curtis, Alex Deng, Weitao Duan, Peter Forbes, Brian Frasca, Tommy Guy, Guido W. Imbens, Guillaume Saint Jacques, Pranav Kantawala, Ilya Katsev, Moshe Katzwer, Mikael Konutgan, Elena Kunakova, Minyong Lee, MJ Lee, Joseph Liu, James McQueen, Amir Najmi, Brent Smith, Vivek Trehan, Lukas Vermeer, Toby Walker, Jeffrey Wong, Igor YashkovMay 19, 2019
Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery
Conference on Information Systems for Crisis Response and Management (ISCRAM)
In this paper, we describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing aggregate pictures of how the population is affected by and responding to the hazard. The maps include insights into evacuations, cell network connectivity, access to electricity, and long-term displacement.
By: Paige Maas, Shankar Iyer, Andreas Gros, Wonhee Park, Laura McGorman, Chaya Nayak, Alex Dow
Connectivity May 14, 2019
Online Learning for Measuring Incentive Compatibility in Ad Auctions
The Web Conference
In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate.
By: Zhe Feng, Okke Schrijvers, Eric Sodomka
Machine Learning May 4, 2019
Understanding Perceptions of Problematic Facebook Use
ACM Conference on Human Factors in Computing Systems (CHI)
While many people use social network sites to connect with friends and family, some feel that their use is problematic, seriously affecting their sleep, work, or life. Pairing a survey of 20,000 Facebook users measuring perceptions of problematic use with behavioral and demographic data, we examined Facebook activities associated with problematic use as well as the kinds of people most likely to experience it.
By: Justin Cheng, Moira Burke, Elena Goetz Davis
Human Computer Interaction & UX May 4, 2019
When Do People Trust Their Social Groups?
ACM CHI Conference on Human Factors in Computing Systems
Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals’ general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups.
By: Xiao Ma, Justin Cheng, Shankar Iyer, Mor NaamanApril 12, 2019
Presto: SQL on Everything
IEEE International Conference on Data Engineering (ICDE)
Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible.
By: Raghav Sethi, Martin Traverso, Dain Sundstrom, David Phillips, Wenlei Xie, Yutian Sun, Nezih Yigitbasi, Haozhun Jin, Eric Hwang, Nileema Shingte, Christopher Berner
Systems & Networking February 21, 2019
Improving Treatment Effect Estimators Through Experiment Splitting
The Web Conference (WWW)
We present a method for implementing shrinkage of treatment effect estimators, and hence improving their precision, via experiment splitting.
By: Dominic Coey, Tom CunninghamDecember 17, 2018
Regression-aware decompositions
SIAM Journal on Matrix Analysis and Applications
Linear least-squares regression with a “design” matrix A approximates a given matrix B via minimization of the spectral- or Frobenius-norm discrepancy ||AX − B|| over every conformingly sized matrix X. Also popular is low-rank approximation to B through the “interpolative decomposition,” which traditionally has no supervision from any auxiliary matrix A.
By: Mark TygertDecember 11, 2018
The Costs of Overambitious Seeding of Social Products
International Conference on Complex Networks and their Applications
Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another.
By: Shankar Iyer, Lada Adamic