Search Results for author: Utkarsh Porwal

Found 7 papers, 0 papers with code

Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

no code implementations27 Dec 2019 Jon Degenhardt, Surya Kallumadi, Utkarsh Porwal, Andrew Trotman

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France.

Information Retrieval

Learning Image Information for eCommerce Queries

no code implementations29 Apr 2019 Utkarsh Porwal

In eBay search, document is an item and the query-item similarity can be computed by comparing different facets of the query-item pair.

Information Retrieval

Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search

no code implementations21 Dec 2018 Grigor Aslanyan, Utkarsh Porwal

We apply this method to eBay search data to estimate click propensities for web and mobile search and compare these with estimates using the EM method.


Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach

no code implementations6 Nov 2018 Utkarsh Porwal, Smruthi Mukund

Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns.

Fraud Detection Outlier Detection +1

Outlier Detection by Consistent Data Selection Method

no code implementations12 Dec 2017 Utkarsh Porwal, Smruthi Mukund

In this work we are proposing an approach that detects outliers in large data sets by relying on data points that are consistent.

Anomaly Detection One-class classifier +1

Spelling Correction as a Foreign Language

no code implementations21 May 2017 Yingbo Zhou, Utkarsh Porwal, Roberto Konow

In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework.

Feature Engineering Language Modelling +3

Parallel Feature Selection Inspired by Group Testing

no code implementations NeurIPS 2014 Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju

Superior performance of our method is demonstrated on a challenging relation extraction task from a very large data set that have both redundant features and sample size in the order of millions.

General Classification Relation Extraction

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