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102 Results

December 16, 2013

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Journal of Machine Learning Research (JMLR)

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such p…

By: Leon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
August 11, 2013

Uncertainty in Online Experiments with Dependent Data: An Evaluation of Bootstrap Methods

ACM Conference on Knowledge Discovery and Data Mining (KDD)

Many online experiments exhibit dependence between users and items. For example, in online advertising, observations that have a user or an ad in common are likely to be associated. Because of this, even in experiments involving millions of subjects, the difference in mean outcomes between control and treatment conditions can have substantial variance. Previous theoretical and simulation results demonstrate that not accounting for this kind of dependence structure can result in confidence intervals that are too narrow, leading to inaccurate hypothesis tests.

By: Eytan Bakshy, Dean Eckles
August 11, 2013

Graph Cluster Randomization: Network Exposure to Multiple Universes

ACM Conference on Knowledge Discovery and Data Mining (KDD)

A drawback with A/B testing is that it is poorly suited for experiments involving social interference, when the treatment of individuals spills over to neighboring individuals along an underlying social network. In this work, we propose a novel methodology using graph clustering to analyze average treatment effects under social interference.

By: Johan Ugander, Brian Karrer, Lars Backstrom, Jon Kleinberg
August 11, 2013

Representing Documents Through Their Readers

ACM Conference on Knowledge Discovery and Data Mining (KDD)

From Twitter to Facebook to Reddit, users have become accustomed to sharing the articles they read with friends or followers on their social networks. While previous work has modeled what these shared stories say about the user who shares them, the converse question remains unexplored: what can we learn about an article from the identities of its likely readers?

By: Khalid El-Arini, Min Xu, Emily Fox, Carlos Guestrin
August 11, 2013

MI2LS: Multi-Instance Learning from Multiple Information Sources

ACM Conference on Knowledge Discovery and Data Mining (KDD)

In Multiple Instance Learning (MIL), each entity is normally expressed as a set of instances. Most of the current MIL methods only deal with the case when each instance is represented by one type of f…

By: Dan Zhang, Jingrui He, Richard Lawrence
July 16, 2013

Selection Effects in Online Sharing: Consequences for Peer Adoption

ACM Conference on Electronic Commerce (EC)

Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one’s adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing.

By: Sean J. Taylor, Eytan Bakshy, Sinan Aral
July 9, 2013

Calling All Facebook Friends: Exploring requests for help on Facebook

AAAI Conference on Weblogs and Social Media (ICWSM)

Past research suggests Facebook use is linked to perceptions of social capital, a concept that taps into the resources people gain from interactions with their social network. In this study, we examin…

By: Nicole Ellison, Rebecca Gray, Jessica Vitak, Cliff Lampe, Andrew Tresolini Fiore
July 8, 2013

Families on Facebook

AAAI Conference on Weblogs and Social Media (ICWSM)

This descriptive study of millions of US Facebook users documents “friending” and communication patterns, exploring parent-child relationships across a variety of life stages and gender combinations.

By: Moira Burke, Lada Adamic, Karyn Marciniak
July 8, 2013

The Anatomy of Large Facebook Cascades

AAAI Conference on Weblogs and Social Media (ICWSM)

When users post photos on Facebook, they have the option of allowing their friends, followers, or anyone at all to subsequently reshare the photo. A portion of the billions of photos posted to Facebook generates cascades of reshares, enabling many additional users to see, like, comment, and reshare the photos.

By: Alex Dow, Lada Adamic, Adrien Friggeri
July 2, 2013

Self-censorship on Facebook

AAAI Conference on Weblogs and Social Media (ICWSM)

We report results from an exploratory analysis examining “last-minute” self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 mil-lion users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them.AAAI Conference on Weblogs and Social Media (ICWSM)

By: Sauvik Das, Adam D. I. Kramer