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Large Scale Learning at Twitter
Published on Aug 13, 201224671 Views
Twitter represents a large complex network of users with diverse and continuously evolving interests. Discussions and interactions range from very small to very large groups of people and most of them
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Chapter list
Large Scale (Machine) Learning at Twitter00:00
Earthquake Los Angeles, July 29, 200803:52
Japan Replies (1)04:51
Japan Replies (2)05:43
What is Twitter06:13
#egypt, #tunesia, #libya, #syria ...07:03
#ocws, #occupywallstreet, ...07:44
The Scale of Twitter08:13
Large scale infrastructure of information delivery08:48
Support for user interaction09:38
Problems we are trying to solve: Relevance10:06
Problems we are trying to solve: Who to follow11:36
Problems we are trying to solve: Content recommendation12:23
(other) problems we are trying to solve12:54
Recommendation/Personalization13:18
Is this BIG data ?13:50
Challenges14:47
What type of machine learning?15:25
ML for social networks (1)16:20
ML for social networks (2)18:15
ML for social networks (2)19:04
Are there wheels not to reinvent?19:22
Analytics Ecosystem20:28
Maximizing the use of Hadoop21:19
AVOID: "janky" analysis of messy data22:17
Leveraging off-line tools23:43
Large scale learning frameworks24:08
Our extensions25:23
Build/reuse/integrate26:28
MapReduce27:06
Java programming28:25
PigML29:14
Training a model in Pig (1)29:35
Training a model in Pig (2)29:53
Training a model in Pig (3)30:05
Applying a model in Pig (1)30:28
Applying a model in Pig (2)30:41
Model training UDF internals31:00
Supervised classification in a nutshell (1)32:08
Supervised classification in a nutshell (2)33:17
Ensembles35:21
Classifier Training / Making Predictions35:48
Ensembles: continued36:59
Further advantages of parallelism37:17
Example: tweet sentiment detection37:27
Diminishing returns ...39:14
Iterative algorithms40:42
Topic modeling42:09
Example: modeling topic distribution42:41
Mahout/PigML integration43:15
LDA applications44:06
Anchoring LDA44:12
ML/Data Mining we contribute to45:12
ML outside of Twitter45:46
Publications mentioning ...46:28
Quick search46:58
Spam/spammer modeling47:17
Example: normal interactions48:59
Example: spammy interactions49:38
ML @ Twitter50:20
Thank you50:46