(MED302) Leveraging Cloud-Based Predictive Analytics to Strengthen Audience Engagement | AWS re:Invent 2014
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In order to improve audience engagement., media companies must deal with vast amounts of raw data from web, social media, devices, catalogs, and back-channel sources. This session dives into predictive analytic solutions on AWS: We present architecture patterns for optimizing media delivery and tuning overall user experience based on representative data sources (video player clickstream, web logs, CDN, user profiles, social media sentiment, etc.). We dive into concrete implementations of cloud-based machine learning services and show how they can be leveraged for profiling audience demand, cueing content recommendations and prioritizing delivery of related media. Services covered include Amazon EC2, Amazon S3, Amazon CloudFront, and Amazon EMR.
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(MED302) Leveraging Cloud-Based Predictive Analytics to Strengthen Audience Engagement | AWS re:Invent 2014
- 1. Michael Limcaco, Amazon Web Services
- 4. Content discovery … and the conversation around it … matter! [1] http://www.slideshare.net/AmazonWebServices/maximizing-audience-engagement-in-media-delivery-med303-aws-reinvent-2013-28622676 [2] http://www.nielsen.com/content/corporate/us/en/press-room/2013/new-nielsen-research-indicates-two-way-causal-influence-between-.html [3] http://www.google.com.au/think/research-studies/quantifying-movie-magic.html
- 5. Search Watch Listen Play DownloadPurchase Contact sales Subscribe Contact support Cancel Rate It Review It UpgradeIt Sharing Tagging Bookmarking SocialSentiment
- 6. •Descriptive –Retrospective –What happened or is happening –Simple aggregations and counters •Predictive –Statistical forecast –Predict a value in a dataset –Machine learning •Prescriptive (emergent) –What should I do about it? Descriptive Predictive Prescriptive
- 7. Machine Learning Signals Predictions
- 12. Storage Visualization & Analysis R Octave Matlab Excel DAS Graphlab Mahout Spark MLlib H20 Hbase HDFS RDBMS SAN/NAS KNIME WEKA Python Kits Single Node Big Data
- 14. Use Case 1
- 16. Spark H20 Recommendation Clustering Classification Math Library Hadoop Map-Reduce
- 17. Estimate similar users and items http://www.slideshare.net/tdunning/recommendation-techn
- 18. User1 Thing1 User2 Thing2 User3 Thing3 User2 Thing4 User5 Thing1 User1 Thing2 User1 Thing3 Mike Jon Mary Phil Kris Logs History Matrix
- 19. History Matrix 2 8 2 4 8 4 Item-Item Matrix
- 20. 2 8 2 4 8 4 Item-Item Matrix LLR Indicators (“Items Similar To This….”)
- 21. Indicators (“Items Similar To This….”) Items Similar To This
- 22. Superman Highlander, Dune Star Wars Raiders, Minority Report Highlander Superman Mulan HomeAlone, Mermaid Star Trek … … … 4587 223, 5234 748 5345, 235 12 8234 245 9543, 7673 3456 4587 … … Index
- 23. Indicators
- 24. 748 Star Wars 45, 235 12 Highlander 8234 245 Mulan 9543, 7673 4587 Superman 12, 5234 3456 Star Trek 2458 … Query“12” 5345 3456 12
- 26. users
- 27. users Media platforms Mobile Search Play Buy Rate Recommendations
- 30. movie-bmovie-c:2.772588722239781 movie-a:2.772588722239781 movie-d…. Indicators (“Items Similar To This….”) % mahout spark-itemsimilarity -iinput-folder/data.txt -o output-folder/ --filter1 buy -fc 1 -ic2 --filter2 view
- 31. Use Case 2
- 32. Classify (estimate) as Positive | Negative http://www.slideshare.net/tdunning/recommendation-techn
- 33. “I thought Star Wars Episode 28 was not without merit ” https://github.com/cyhex/streamcrab
- 34. users
- 35. users Media platforms Mobile Search Play Buy Rate Recommend Social Media activity
- 36. Extract Features Classify Extract Features Classify Extract Features Classify Model Training PositiveNegative “I adored this movie” “adore”= POSITIVE
- 37. Extract Features Classify Extract Features Classify Extract Features Classify Model Training PositiveNegative
- 38. http://www.nltk.org/book/ch06.htmlTextBlob + Natural Language Toolkit (NLTK) 1 2
- 39. from textblob.classifierimport NaiveBayesClassifier training_data= [(‘I love this movie’, ‘Positive’), (‘This makes me mad ’, ‘Negative’) …] my_classifier= NaiveBayesClassifier(training_data)
- 40. “I thought Star Wars Episode 29 was not without merit ” “Positive” from amazon_kclpy import kcl import json, base64 class RecordProcessor(kcl.RecordProcessorBase): def process_records(self, records, checkpointer): : inbound_tweet = base64.b64decode(record.get(‘data’)) sentiment = my_classifier.classify(inbound_tweet)
- 41. Extract Features Classify Extract Features Classify Extract Features Classify Model Training PositiveNegative
- 43. 12 2 7 85 1 99 Mulan 1 5 99 85 50 4 Mulan 1 2 3 4 5 6 Mulan 3 1 4 6 7 9 Mulan
- 46. Use Case 3
- 47. This is a form of unsupervised learning
- 48. Segaran, Toby. Programming Collective Intelligence. Sebastopol: O’Reilly, 2009. Print.
- 51. R + H20
- 52. R + H20 Data Science Desktop Machine Learning Cluster
- 53. R + H20 % java –jar h20.jar
- 56. Use Case 4
- 58. Customer Geo Account Type AccountAge SupportTickets Minutesstreamed Churn? Mike CA Premium 120 10 240 TBD John CA Basic 240 1 140 TBD Ingrid WA Premium 60 5 1800 TBD Mark WA Basic 30 0 0 TBD Usman WA Basic 720 0 360 TBD
- 62. AWS Marketplace Software •BigML •Revolution R Enterprise •PredictionIO •Yhat •Mortar •Zementis