06_PeopleDiscovery.FB

[|Andreas Weigend] MS&E 237, Stanford University, Spring 2010
 * Social Data Revolution**

=Class 6: People Discovery= Class Date: April 15, 2010 [|Audio] | [|Transcript] Powerpoint: NA Paper: NA

PHAME What works, what doesn't Metrics of relevance Tradeoffs: explicit Trust: why should ppl trust FB?

4:15 Intro / Summary last class

4:20 User perspective: Discovering ppl, why? Why on FB vs twitter? How is this working for you?

facebook.com/socialdatarevolution POST: Vs MrTweet

4:30 Best of Lars http://www.cs.cornell.edu/~lars/www07-anon.pdf May 2007 Privacy
 * what are the simple insights / one non-super tech paper?
 * Able to deduce links using annoymous social graphs via key injection
 * Therefore, no desire to release social graph and have company appear on NYT next day as a result.

4:45 PYMK

P: The problem FB perspective (engagement increase, more relevant content, more ads :) H: based on prior work on network evolution A: try different algos for relevance
 * most links close triangles (A and E know each other, E knows L, A now links with L)
 * what we should do is suggest friends of friends
 * how to pick / data mining / machine learning
 * features
 * training strategies - identifying the target goal is important to nail the training strategies
 * bagged decision trees - a simple machine learning algo for training the system
 * performance - real time algo vs offline algo (mix them to get best results)

5:00 ASW M: 5:15 Lars: E: Graphs, showing improvements
 * 5' exericse on metrics
 * 5' collection
 * CTR on friend
 * total nr of clicks, impression
 * conversion (ie add)
 * unfriending (returns)
 * stockpiling
 * Value of frienships: how to measure (1:1 messages, invites to events, subsequent network growth), incremental increase
 * Social capital delta: for BOTH sides
 * 2 ML systems time scales, one on top of the other

5:20 Questions? 5:25 "Take aways"
 * Goals matter (Optimization problem)
 * Ecosystem matters (Cost of irrelevance, Metrics matter)
 * Time scales of computation

5:30 End Bozhi See seebozhi@gmail.com Tal Rusak tal@cs.stanford.edu media type="custom" key="5928861"