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.


CIKM ’13 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management Pages 1137-1146

On Segmentation of eCommerce Queries

Nish Parikh, Prasad Sriram, Mohammad AlHasan

In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers′ data and also frequency data from product titles what we call sellers′ data.

We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation.

Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.

in Proceedings of the 22nd international conference on World Wide Web (WWW ’13)

Anatomy of a Web-Scale Resale Market: A Data Mining Approach

Yuchen Zhao, Neel Sundaresan, Zeqian Shen, Philip Yu

Reuse and remarketing of content and products is an integral part of the internet. As E-commerce has grown, online resale and secondary markets form a significant part of the commerce space. The intentions and methods for reselling are diverse. In this paper, we study an instance of such markets that affords interesting data at large scale for mining purposes to understand the properties and patterns of this online market.

As part of knowledge discovery of such a market, we first formally propose criteria to reveal unseen resale behaviors by elastic matching identification (EMI) based on the account transfer and item similarity properties of transactions.

Then, we present a large-scale system that leverages MapReduce paradigm to mine millions of online resale activities from petabyte scale heterogeneous ecommerce data. With the collected data, we show that the number of resale activities leads to a power law distribution with a ‘long tail’, where a significant share of users only resell in very low numbers and a large portion of resales come from a small number of highly active resellers.

We further conduct a comprehensive empirical study from different aspects of resales, including the temporal, spatial patterns, user demographics, reputation and the content of sale postings. Based on these observations, we explore the features related to “successful” resale transactions and evaluate if they can be predictable.

We also discuss uses of this information mining for business insights and user experience on a real-world online marketplace.

To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.

Large-Scale Video Summarization Using Web-Image Priors

Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan
Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as a prior to facilitate summarization of user-generated videos.
Our main intuition is that people tend to take pictures of objects to capture them in a maximally informative way. Such images could therefore be used as prior information to summarize videos containing a similar set of objects.
In this work, we apply our novel insight to develop a summarization algorithm that uses the web-image based prior information in an unsupervised manner. Moreover, to automatically evaluate summarization algorithms on a large scale, we propose a framework that relies on multiple summaries obtained through crowdsourcing.
We demonstrate the effectiveness of our evaluation framework by comparing its performance to that of multiple human evaluators. Finally, we present results for our framework tested on hundreds of user-generated videos.
SIGIR 2013: 193-202

Faster and smaller inverted indices with treaps.

Roberto Konow, Gonzalo Navarro, Charles L. A. Clarke, Alejandro López-Ortiz

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.

Data Compression Conference 2013: 351-360

Faster Compact Top-k Document Retrieval

Roberto Konow, Gonzalo Navarro:

An optimal index solving top-k document retrieval [Navarro and Nekrich, SODA'12] takes O(m+k) time for a pattern of length m, but its space is at least 80n bytes for a collection of n symbols. We reduce it to 1.5n-3n bytes, with O(m + (k+log log n)log log n) time, on typical texts. The index is up to 25 times faster than the best previous compressed solutions, and requires at most 5% more space in practice (and in some cases as little as one half). Apart from replacing classical by compressed data structures, our main idea is to replace suffix tree sampling by frequency thresholding to achieve compression.


Physician Incentives and Treatment Choices in Heart Attack Management

Dominic Coey

We estimate how physicians’ financial incentives affect their treatment choices in heart Attack management, using a large dataset of private health insurance claims. Different insurance plans pay physicians different amounts for the same services, generating the required variation in financial incentives.

We begin by presenting evidence that, unconditionally, plans that pay physicians more for more invasive treatments are associated with a considerably larger fraction of such treatments. To interpret this correlation as causal, we continue by showing that it survives conditioning on a rich set of diagnosis and provider-specific variables.

We perform a host of additional checks verifying that differences in unobservable patient or provider characteristics across plans are unlikely to be driving our results. We find that physicians’ treatment choices respond positively to the payments they receive, and that the response is quite large.

If physicians received bundled payments instead of fee-for-service incentives, for example, heart attack management would become considerably more conservative. Our estimates imply that 20 percent of patients would receive different treatments, physician costs would decrease by 27 percent, and social welfare would increase.

To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Mobile Vision Workshop, 2013.

Style Finder: Fine-Grained Clothing Style Recognition and Retrieval

Wei Di, Catherine Wah, Anurag Bhardwaj, Robinson Piramuthu, Neel Sundaresan

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text.

However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem.

First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles.

We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.


Palette Power: Enabling Visual Search through Colors

Anurag Bhardwaj, Atish DasSarma, Wei Di, Raffay Hamid, Robinson Piramuthu, Neel Sundaresan

xplosion of mobile devices with cameras, online search has moved beyond text to other modalities like images, voice, and writing. For many applications like Fashion, image-based search offers a compelling interface as compared to text forms by better capturing the visual attributes.

In this paper we present a simple and fast search algorithm that uses color as the main feature for building visual search. We show that low level cues such as color can be used to quantify image similarity and also to discriminate among products with different visual appearances.

We demonstrate the effectiveness of our approach through a mobile shopping application (eBay Fashion App available at and eBay image swatch is the feature indexing millions of real world fashion images).

Our approach outperforms several other state-of-the-art image retrieval algorithms for large scale image data.

To appear in Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR) 2013

Dense Non-Rigid Point-Matching Using Random Projections

Raffay Hamid, Dennis DeCoste, Chih-Jen Lin

We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations. Our main intuition is that the subset of points that can be matched with high confidence should be used to guide the matching procedure for the rest.

We propose a novel algorithm that incorporates these high-confidence matches as a spatial prior to learn a discriminative subspace that simultaneously encodes both the feature similarity as well as their spatial arrangement.

Conventional subspace learning usually requires spectral decomposition of the pair-wise distance matrix across the point-sets, which can become inefficient even for moderately sized problems. To this end, we propose the use of random projections for approximate subspace learning, which can provide significant time improvements at the cost of minimal precision loss.

This efficiency gain allows us to iteratively find and remove high-confidence matches from the point sets, resulting in high recall. To show the effectiveness of our approach, we present a systematic set of experiments and results for the problem of dense non-rigid image-feature matching.