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387 Results

August 1, 2009

Hive – A Warehousing Solution Over a Map-Reduce Framework

International Conference on Very Large Data Bases (VLDB)

The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop is a popular o…

By: Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Suresh Antony, Hao Liu, Pete Wyckoff
June 1, 2009

Feed Me: Motivating Newcomer Contribution in Social Network Sites

ACM Conference on Human Factors in Computing Systems

Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution.

By: Moira Burke, Cameron Marlow, Thomas Lento
April 1, 2009

Gesundheit! Modeling Contagion through Facebook News Feed

AAAI Conference on Weblogs and Social Media

Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades.

By: Eric Sun, Itamar Rosenn, Cameron Marlow, Thomas Lento
September 8, 2017

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Conference on Empirical Methods on Natural Language Processing (EMNLP)

Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states.

By: Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra
September 27, 2017

Forecasting at Scale

The American Statistician 2017

To address the challenges of producing reliable and high-quality data science forecasts, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis.

By: Sean J. Taylor, Benjamin Letham
February 24, 2018

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

International Symposium on High-Performance Computer Architecture (HPCA)

Facebook’s machine learning workloads are extremely diverse: services require many different types of models in practice. This paper describes the hardware and software infrastructure that supports machine learning at global scale.

By: Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang
November 1, 2017

Measuring and Mitigating OAuth Access Token Abuse by Collusion Networks

ACM Internet Measurement Conference (IMC)

We uncover a thriving ecosystem of large-scale reputation manipulation services on Facebook that leverage the principle of collusion.

By: Shehroze Farooqi, Fareed Zafar, Nektarios Leontiadis, Zubair Shafq