Friday, June 29, 2018

KDD 2018: Anatomy of a Privacy-Safe Large-Scale Information Extraction System Over Email

I'm excited to share my team's work that will be presented at KDD 2018. Information extraction can be a hard problem, and doing that in a privacy-safe manner from email harder yet. Our paper lays out how to build such a system.




Abstract from the paper:

Extracting structured data from emails can enable several assistive experiences, such as reminding the user when a bill payment is due, answering queries about the departure time of a booked flight, or proactively surfacing an emailed discount coupon while the user is at that store. This paper presents Juicer, a system for extracting information from email that is serving over a billion Gmail users daily. We describe how the design of the system was informed by three key principles: scaling to a planet-wide email service, isolating the complexity to provide a simple experience for the developer, and safeguarding the privacy of users (our team and the developers we support are not allowed to view any single email). We describe the design tradeoffs made in building this system, the challenges faced and the approaches used to tackle them. We present case studies of three extraction tasks implemented on this platform—bill reminders, commercial offers, and hotel reservations—to illustrate the effectiveness of the platform despite challenges unique to each task. Finally, we outline several areas of ongoing research in large-scale machine-learned information extraction from email.

Saturday, February 3, 2018

ICDE 2018: Recommendations for All : Solving Thousands of Recommendation Problems Daily

I'm excited to share that our paper on scaling a recommendation servicewas accepted to ICDE 2018! An ML+Systems perspective.
Recommender systems are a key technology for many online services includinge-commerce, movies, music, and news. Online retailers use product recommender systemsto help users discover items that they may like. However, building a large-scaleproduct recommender system is a challenging task. The problems of sparsity andcold-start are much more pronounced in this domain. Large online retailers have usedgood recommendations to drive user engagement and improve revenue, but the complexityinvolved is a roadblock to widespread adoption by smaller retailers.
In this paper, we tackle the problem of generating product recommendations for
tens of thousands of online retailers. Sigmund is an industrial-scale system forproviding recommendations as a service. Sigmund was deployed to production in early2014 and has been serving retailers every day. We describe the design choices that wemade in order to train accurate matrix factorization models at minimal cost.We also share the lessons we learned from this experience -- both from a machinelearning perspective and a systems perspective. We hope that these lessons areuseful for building future machine-learning services.

Wednesday, January 17, 2018

WWW 2018 : Hidden in Plain Sight – Classifying Emails Using Embedded Image Contents

I'm excited to share that my team's paper on classifying emails using embedded image contents has been accepted to WWW 2018 (aka The Web Conference 2018).

In this paper, we tackle the problem of extracting information from commercial emails promoting an offer to the user.  The sheer number of these promotional emails makes it difficult for users to read all these emails and decide which ones are actually interesting and actionable.  Extracting information  enables an email platform to build several new experiences that can unlock the value in these emails without the user having to navigate and read all of them. For instance, we can highlight offers that are expiring soon, or display a notification when there’s an unexpired offer from a merchant if your phone recognizes that you are at that merchant’s store.

A key challenge in extracting information from such commercial emails is that they are often image-rich and contain very little text. Training a machine learning (ML) model on a rendered image-rich email and applying it to each incoming email can be prohibitively expensive. In this paper, we describe a cost-effective approach for extracting signals from both the text and image content of commercial emails in the context of a free email platform that serves over a billion users around the world. The key insight is to leverage the template structure of emails, and use off-the-shelf OCR techniques to obtain the text from images to augment the existing text features offline. Compared to a text-only approach, we show that we are able to identify 9.12% more email templates corresponding to ~5% more emails being identified as offers. Interestingly, our analysis shows that this 5% improvement in coverage is across the board, irrespective of whether the emails were sent by large merchants or small local merchants, allowing us to deliver an improved experience for everyone.


Thursday, July 6, 2017

KDD 2017 Teaser Video and Paper

Check out the teaser video for our paper at KDD 2017!  The PDF of the paper is available here at the Google Research web site.


Tuesday, May 30, 2017

Drive Quick Access at KDD 2017

Our paper, "Quick Access: Building a Smart Experience for Google Drive" was accepted for presentation at KDD 2017. The development of the Quick Access feature illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine-learning in mind, and evolving product definitions. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine-learning problems. We're looking forward to sharing this with the KDD community.

Friday, March 10, 2017

Quick Access on Drive: How Machine Learning can save you time

Check out my blog post on the Google Research blog about how ML saves you time with Quick Access on Drive.
Quick Access on Drive (Web and iOS)

Thursday, September 29, 2016

Quick Access: Machine Learning in Google Drive

I'm very excited about a new feature in Drive that our team in Research/Machine Intelligence helped launch today: Quick Access for Drive.



Quick access uses Google Machine learning on your Drive Activity, and your daily interactions to bring you the documents you need before you try to search/navigate to it. Our user-research shows that it shaves 50 percent off the average time it takes to get to the right file. Quick Access is available for G Suite customers on Android. If you're interested in all the new intelligent features in Google Apps, check out this post from Prabhakar Raghavan, the VP for Google Apps.