We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.


SDM 2012

Multi-Skill Collaborative Teams based on Densest Subgraphs

Atish Das Sarma, Amita Gajewar, Atish Das Sarma, Amita Gajewar

We consider the problem of identifying a team of skilled individuals for collaboration, in the presence of a social network, with the goal to maximize the collaborative compatibility of the team. Each node in the social network is associated with skills, and edge-weights specify affinity between respective nodes. We measure collaborative compatibility objective as the density of the induced subgraph on selected nodes.

This problem is NP-hard even when the team requires individuals of only one skill. We present a 3-approximation algorithm for the single-skill team formulation problem. We show the same approximation can be extended to a special case of multiple skills.

Our problem generalizes the formulation studied by Lappas et al. [KDD ’09] who measure team compatibility in terms of diameter or spanning tree. The experimental results show that the density-based algorithms outperform the diameter-based objective on several metrics.


Interactive regret minimization

Danupon Nanongkai, Ashwin Lall, Atish Das Sarma, Kazuhisa Makino

We study the notion of regret ratio proposed in [19] Nanongkai et al. [VLDB10] to deal with multi-criteria decision making in database systems. The regret minimization query proposed in [19] Nanongkai et al. was shown to have features of both skyline and top-k:

it does not need information from the user but still controls the output size. While this approach is suitable for obtaining a reasonably small regret ratio, it is still open whether one can make the regret ratio arbitrarily small. Moreover, it remains open whether reasonable questions can be asked to the users in order to improve efficiency of the process.

In this paper, we study the problem of minimizing regret ratio when the system is enhanced with interaction. We assume that when presented with a set of tuples the user can tell which tuple is most preferred.

Under this assumption, we develop the problem of interactive regret minimization where we fix the number of questions and tuples per question that we can display, and aim at minimizing the regret ratio. We try to answer two questions in this paper:

(1) How much does interaction help? That is, how much can we improve the regret ratio when there are interactions?

(2) How efficient can interaction be? In particular, we measure how many questions we have to ask the user in order to make her regret ratio small enough.

We answer both questions from both theoretical and practical standpoints. For the first question, we show that interaction can reduce the regret ratio almost exponentially. To do this, we prove a lower bound for the previous approach (thereby resolving an open problem from [19] Nanongkai et al.), and develop an almost-optimal upper bound that makes the regret ratio exponentially smaller.

Our experiments also confirm that, in practice, interactions help in improving the regret ratio by many orders of magnitude. For the second question, we prove that when our algorithm shows a reasonable number of points per question, it only needs a few questions to make the regret ratio small.

Thus, interactive regret minimization seems to be a necessary and sufficient way to deal with multi-criteria decision making in database systems.

Design, Guidelines, and Requirements, Personal and Ubiquitous Computing, 2012

Live Mobile Collaboration for Video Production

Marco deSa, David Shamma, Elizabeth Churchill

Traditional cameras and video equipment are gradually losing the race with smart-phones and small mobile devices that allow video, photo and audio capturing on the go. Users are now quickly creating movies and taking photos whenever and wherever they go, particularly at concerts and events.

Still, in-situ media capturing with such devices poses constraints to any user, especially amateur ones. In this paper, we present the design and evaluation of a mobile video capture suite that allows for cooperative ad-hoc production. Our system relies on ad-hoc in-situ collaboration offering users the ability to switch between streams and cooperate with each other in order to capture better media with mobile devices.

Our main contribution arises from the description of our design process focusing on the prototyping approach and the qualitative analysis that followed. Furthermore, we contribute with lessons and design guidelines that emerged and apply to in-situ design of rich video collaborative experiences and with the elicitation of functional and usability requirements for collaborative video production using mobile devices.

Journal of the American Society for Information Science and Technology (JASIST), 2012

Automatic Identification of Personal Insults on Social News Sites

Sara OwsleySood, Elizabeth Churchill, Judd Antin

As online communities grow and the volume of user-generated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors,

and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task?

In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions.

The system shows great potential for semiautomated community management.


Bayes-Nash Equilibria of the Generalized Second-Price Auction

Renato Gomes

We develop a Bayes–Nash analysis of the generalized second-price (GSP) auction, the multi-unit auction used by search engines to sell sponsored advertising positions. Our main result characterizes the efficient Bayes–Nash equilibrium of the GSP and provides a necessary

and sufficient condition that guarantees existence of such an equilibrium. With only two positions, this condition requires that the click–through rate of the second position is sufficiently smaller than that of the first.

When an efficient equilibrium exists, we provide a necessary and sufficient condition for the auction revenue to decrease as click–through rates increase. Interestingly, under optimal reserve prices, revenue increases with the click–through rates of all positions. Further, we prove that no inefficient equilibrium of the GSP can be symmetric.

Our results are in sharp contrast with the previous literature that studied the GSP under complete information.