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Brian Johnson

Media Swapping Patent

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Pinterest has the world’s largest catalog of human curated ideas. We’re building a visual discovery engine with 100+ billion ideas, collected by 175+ million people worldwide. As we work to match the right Pin to the right person at the right time, personalization is crucial. Random graph walks with restart are an excellent way to surface popular, high quality, relevant content. But we can also show you great ideas you may not even have known you were looking for - and that’s where vector embedding comes in. We embed you and these billions of ideas in a 128 or 256 dimensional space. Then we project them down into 1000 bits, cut them up into 16 bit chunks, index these chunks, and then find these ideas for you really fast using core search technology. Bio Brian joined Pinterest in 2017 as the Head of Knowledge. He was previously at eBay, Handspring, Excite@Home, Synopsys, and AT&T Bell Labs. Brian received his Ph.D. in Computer Science from the University of Maryland. His original Treemap data visualization paper has been cited thousands of times.

Graph Walks & Vector Embeddings: Exploiting the head and exploring the tail

Brian Johnson

eBay Search Science: Leveraging Behavioral Data Analysis for Effective Query Reformulation Brian will talk about combing through behavioral log files with Scala on Hadoop in order to generate large data sets used to drive dynamic, online query rewrites at eBay. He’ll cover the product/feature pipeline from ideation to data mining, prototyping, statistical analysis, offline side by side analysis, human judgment, online experimentation, and finally launch.

eBay Search Query Intent

Brian Johnson

2015-04 eBay Statistics

Brian Johnson

Topic: eBay Search Science: Leveraging Behavioral Data Analysis for Effective Query Reformulation Brian will talk about combing through behavioral log files with Scala on Hadoop in order to generate large data sets used to drive dynamic, online query rewrites at eBay. He’ll cover the product/feature pipeline from ideation to data mining, prototyping, statistical analysis, offline side by side analysis, human judgment, online experimentation, and finally launch. Time permitting he will also touch on statistical machine translation based spell correction and machine learned search spam detection.

eBay Search Science, IEEE Big Data, April 3rd, 2015

Brian Johnson

CloudCon Data Mining Presentation

Brian Johnson

The New Alchemy: Turning Data into Gold Developers are leading the charge to turn consumer behavior into profitable solutions. By accessing and analyzing the explosion of data from consumer activities, any developer can create the personalized, relevant products and services that customers demand and merchants urgently need. We will discuss how to acquire, store, and mine information, and how to design analytics-focused software and build data-driven software engines.

2011 x.commerce Innovate Data Alchemy

Brian Johnson

11 964 181 System And Method For Providi

Brian Johnson

10 977 279 Method And System For Categor

Brian Johnson

11 869 290 Electronic Publication System

Brian Johnson

July 27, 2011 Bay Area Search Presentation Brian Johnson, Engineering Director, Query Services @ eBay Query expansion is an important part of of the search recall for all search engines. In this talk I'll discuss some of the general trend driving Hadoop adoption within the Search Query Services team at eBay, and the types of algorithms/techniques we've moved to Hadoop at eBay. Over time we've moved from smaller, editorial data sets to large machine generated data sets mined from behavior log data, items/listings, catalogs, etc. One common workflow is to mine large candidate rewrites/expansions data sets from multiple data sources, use crowd sourced human judgment to classify a subset of the candidates (true positive, false positive), use machine learning techniques discard false positives, run automated validation on the final data set, and automatically push to production. Ravi Jammalakadaka, Senior Applied Researcher, Query Services @ eBay Ravi is a real engineer. Not a pointy haired manager like the previous speaker. Expect some real engineering:-) He'll be doing a literature review for acronym mining and discussing a real world implementation. Title: Mining Acronyms From Raw Text Abstract: Significant number of eBay products are known by their acronyms. eBay query expansion service expands user queries by their acronym equivalents to increase recall. The challenge is to mine acronyms from either seller ( ex. item descriptions, titles) or buyer ( ex. queries) data. Ravi will present the state of the art algorithms from recent conferences that mine acronyms from raw text and present their limitations. He will present a new acronym mining algorithm that seeks to address the limitations identified with previous algorithms. He will present a machine learning classifier that seeks to remove the false positives generated from the acronym mining algorithm.

2011 Search Query Rewrites - Synonyms & Acronyms

Brian Johnson

More from Brian Johnson (10)

Graph Walks & Vector Embeddings: Exploiting the head and exploring the tail

eBay Search Query Intent

2015-04 eBay Statistics

eBay Search Science, IEEE Big Data, April 3rd, 2015

CloudCon Data Mining Presentation

2011 x.commerce Innovate Data Alchemy

11 964 181 System And Method For Providi

10 977 279 Method And System For Categor

11 869 290 Electronic Publication System

2011 Search Query Rewrites - Synonyms & Acronyms

11 641 262 Proprietor Currency Assignmen

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