Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

GeoMonday 2015.4 - Shortening the Distance in Local Search

982 views

Published on

Yelp, Foursquare, and many others have paved the way for consumers and local businesses to interact with each other. These services give context to the eternal question “Where should I go?” In this session, Scoped.co founder David Yu will talk about how to “shorten the distance” between a user and what they want. For example, how does one answer the question “What handbags are available close to me?” David will describe some of the data, search and recommendation problems he has worked on, and how Scoped plans to take local search one step further, not only to help consumers find what they want, but also to empower brick and mortar businesses.

* Data science is not a buzz word – examples from Bioinformatics and Goodreads
* Personalized search – Scoped.co Demo #1
* Defining the data topology for the physical world
* What can we do with data? – Scoped.co Demo #2

David Yu is a serial entrepreneur. His first company Discovereads was acquired by Goodreads/Amazon, and the technology currently powers book discovery for more than 50 million users world wide. He also worked as the CTO of Q Digital, a media company with 10 million MAUs. Scoped is his third company. David double-majored in computer science and molecular biophysics at Yale University, and received his JD degree from Harvard Law School. Before becoming an entrepreneur, David worked for Genentech’s Bioinformatics department, using computer algorithms to find potential drug targets. In his spare time, David enjoys skiing, playing the cello, and going to rock and classical concerts.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

GeoMonday 2015.4 - Shortening the Distance in Local Search

  1. 1. David Yu, david@scoped.co Shortening the Distance in Local Search
  2. 2. “Big Data” “Machine Learning” “Recommendation Engine” “Helene Fischer”
  3. 3. • Made Biotech Drugs • Bioinformatics Department • Genomics & Proteomics Data
  4. 4. Recommendations & The Netflix Prize # items tech Movies 18K C/C++/Java Books 200K C++ Places 2m Hadoop
  5. 5. Demo #1
  6. 6. But…. This is only an improvement Yelp & Foursquare are good enough
  7. 7. Rethinking…. Anything else we can do for users and local businesses? I went back to my bioinformatics roots
  8. 8. Local Search Currently
  9. 9. Demo #2
  10. 10. In the future….. Building out relationships between objects
  11. 11. Thank You! and I need help… david@scoped.co

×