Andreas Weigend, Social Data Revolution | MS&E 237, Stanford University, Spring 2011 | Syllabus

Class_01: Introduction To SDR, Course Logistics

Date: March 29, 2011
Audio: weigend_stanford2011.01_2011.03.29.mp3
Initial authors:Alan Guo,; Chris Sholley,; Arash Ghodoosi,;

Key Points

  • Essence of social data revolution is collaboration/co-creation.
    • Who are we? We are what we share.
    • Sharing allows for matching
      • Get paid: Atomize work so that workers/volunteers can focus on what inspires them, i.e., what they are interested in and good at. (Andreas is an advisor to
      • Get laid: Dating sites (Andreas is on the board of mobile dating app (was class project last year)
      • MoodLogic engages users to create meta-data
  • Data ownership: physical objects are exclusive; data are not--they could be owned by a network; data ownership is no longer with a person or a firm
  • Social data can reshape how people and organizations interact with each other. Examples:
    • VillageVines, real-time pricing & yield management
    • Avis rental car experience:
      • rented car and changed drop-off by phone for one-way trip between Long Beach(TED) and Palm Springs
      • charged 5 times what he expected supposedly b/c changed was not recorded on Avis’ system
      • Suggest fairness by increasing transparency / reducing information asymmetry, Give access to call record to caller, not only to company.
      • Beyond sharing with the individual, give access to world, i.e., socialized customer service calls could be socialized; providing more data to customers lead to improvement of customer relationship;
  • Customers interact with companies virtually only, not physically anymore.
    • Amazon: online companies don’t have their HQ & storage in the same place;
  • Content should be broadly(but selectively)-writable and universally accessible
    • BestBuy: all employees and customers should be able to write and provide feedbacks about the products (like Amazon)
    • our course Wiki


Instructor's Background

1. CERN 86. undergrad, studied high energy physics.;
2. Started PhD at SLAC 86. Advised by Jonathan Dorfan. Took CS class by Terry Winograd "Understanding Computers and Cognition". Inspired by Parallel Distributed Processing (PDP). Interested in learning from data.;
3. Did thesis studying behavior of wall street traders with co-author of PDP, David Rumelhart;
4. Taught in Thailand for half-year after PhD. Twenty years later worked with a startup in Thailand, recently sold to Priceline;
5. Xerox PARC postdoc, Assitant Prof CS (Univ of Colorado), Associate Prof NYU (Information Systems)
5. First startup: Moodlogic: Music recommendation system. Strategy: get users to create metadata; (metada are data created to describe another form of data, i.e. music, movie, food)
6. Chief Scientist at Amazon: not big company person. Key learning was that Amazon profited from recommendation data. They designed system that experimented and tracked user experience. Important to think about data strategy. i.e. How to acquire data?.

In Amazon’s case, AOL was much larger at the time, but approached Amazon to provide recommendations. Because AOL lacked the ability to perform user data based recommendation, it was actually dependent on Amazon. Amazon was able to be exposed to a much larger set of data, and rapidly improved their recommendation engine.

7. Teaching Social Data Revolution @ Stanford in the past 6 years.;
8. Investor: Primary investor in Xiaonei (Chinese Facebook clone). Partner in Founders Fund (started by Peter Thiel), invested in Sean Parker’s fund;
9. Works with big companies: CEO of Lufthansa (customer loyalty service) called on Christmas day, Best Buy (more people should be able to write to database about products, Best Buy created Blue Shirt Nation - employees write about products);
10. Works with small companies: VillageVines (yield management applied to restaurants), Palantir (companies related the funds he invested with);
11. His schedule has been online since 1994:

Student Responsibilities for Course

For Participation

  • Co-create course wiki;
  • Give feedback to class representative;
  • Be prepared for questions from visiting speakers;
  • Get elected to be a class representative: meet four times a quarter with Professor Weigend, to provide early feedback;
  • Join former students in Social Data Lab: Prepare for meeting once a qtr to co-create with big companies on how to prepare for future. e.g. product development;

For Grade

Submit your suggestions for guest speakers at