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Audience projection of target consumers over multiple domains a ner and bayesian approach, Gianmario Spacagna, Alberto Pirovano
The document discusses how Helixa utilizes artificial intelligence, specifically named entity recognition (NER) and Bayesian models, to enhance market research by integrating various consumer data sources. It highlights the limitations of traditional market research methods and proposes a novel approach to accurately project target audiences based on consumer behavior insights. The presentation outlines the mathematical frameworks and algorithms involved in audience projection, aiming to provide brands with a comprehensive understanding of their consumers.
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Audience projection of target consumers over multiple domains a ner and bayesian approach, Gianmario Spacagna, Alberto Pirovano
- 1. Helixa Audience Projection ofTarget Consumers over Multiple Domains: a NER and Bayesian approach Gianmario Spacagna Chief Scientist @ Helixa O’Reilly AI Conference London, 16th October 2019
- 2. About Me 7+ yearsexperience in Data Science and Machine Learning Currently leading a team of ML Scientists and ML Engineers Background in Telematics and Software Engineering of Distributed Systems Ongoing MBA Student Co-author of Python Deep Learning Contributor of the Professional Data Science Manifesto Blogger of Data Science Vademecum Founder of the Data Science Milan community (1.4k members) Stockholm, London, Milan Gianmario Spacagna Chief Scientist, Helixa gspacagna@helixa.ai
- 3. DEMOGRAPHICS HHI < 40K Female 18- 24 INFLUENCERS ODESZA Cardi B Shane DawsonJames Charles Helixa is Market Research platform that uses AI to integrate disparate data sources into an enriched view of the consumers who matter to your business. INTERESTS Listen to Podcasts Kylie Cosmetics Fan Starbucks Chipotle PSYCHOGRAPHICS Fast Food Fans Fashion Enthusiasts Entertainment Junkies
- 4. In the next40 minutes... OUR GOAL: Discuss some of the current challenges of traditional market research and propose a novel solution based on Named Entity Recognition (NER) and Bayesian Inference.
- 5. Challenges in MarketResearch
- 6. Applied Social Science Whatis Market Research? Gain Insights for Strategic Decisions Information about individuals and organizations Statistical Inference
- 7. Why Market Researchmatters? Brands Perceptions Consumers Preferences and Behaviors Buyer Personas Market Segmentation Identify OpportunitiesMarket Trends
- 8. Approaches to MarketResearch Opinions and individual experiences In-depth interviews Smaller sample Qualitative Quantitative Numbers and Data Statistics Larger sample
- 9. Quantitative Market Researchis conducted with Surveys Define Analyze DistributeCollect Design
- 10. Limitations of Surveys Expensive Invasive ResponseBias Predefined questions Narrow coverage
- 11. Market Research using“Implicit Consumers Feedback” Define Analyze DistributeCollect Design vs. e.g. Social Listening
- 12. Twitter Interactions Inferring Interestsfrom Twitter Interactions
- 13. Advantages of ImplicitConsumer Feedback Approaches Flexible costs Wide view Opportunities for Big Data and AIMass coverage Spontaneous
- 14. What about otherinformation? Twitter Interactions Amazon Purchases ? Beer Consumption Brand ?
- 15. The Universe ofConsumers Datasets Social Media Financial and Properties Behaviors First Party (CSM) Consumer Research Surveys
- 16. SCATTERED PARTIAL SKEWED MA L E F E M A L E 18-30 31-43 44-56 57-70 Individual Consumers Datasets are Far From Being Exhaustive
- 17. ALL IN ONECOMPLETE REPRESENTATIVE M A L E F E M A L E 18-30 31-43 44-56 57-70 The Holy Grail of Market Research
- 18. What is thebaseline algorithm for “completing” datasets?
- 19.
- 20. What is look-alikefusion? Left: Social Network Panel Right: Consumptions Survey Panel
- 21. Assignment Optimization Problem ●Hungarian method ● Simplex ● Auction algorithm Well-known solutions:
- 22. Datasets Fusion X X XX X X X X X X X Left User Right User left-only entities right-only entities Target Audience =
- 23. Look-alike Fusions Requiresa Main Panel Centrality
- 24. Look-alike Fusions Don’tScale Well Differences in feature space Craftsmanship required at each change of data Universal objective function to optimize
- 25. Is there amore scalable way to “fuse” datasets?
- 26.
- 27. Audience Projection definedas “User Binary Classification” Source: Social Network Panel Destination: Consumptions Survey Panel 70M Social accounts 200M U.S. consumers 1.6M / 26M / TRUE FALSE TRUE FALSE Target Audience = PROJECTION Ben & Jerry’s: bought in last 6 months? Affinity: 1.80x Venmo: paid in last 30 days? Affinity: 1.6x Angry Orchard: drunk in last 6 months? Affinity: 1.50x
- 28. Solution = NamedEntity Recognition (NER) + Bayesian Model Social Pages Consumption Questions NER NER BAYESIAN MODEL ENTITY LINKING (NEL) Destination: Consumptions Survey Panel Source: Social Network Panel Projected Users Probabilities Target Audience
- 29. Entities Represent anUniversal Feature Space Social Pages Consumption Questions Listed Products NER NER NER
- 30. The Coca-Cola Companyis a total beverage company, offering over 500 brands in more than 200 countries and territories. Named Entity Recognition(NER) in each Domain Social Pages Consumption Questions Listed Products Adidas Originals Men's Relaxed Strapback Cap Coca-Cola KWC-4 6-Can Personal Mini 12V DC Car and 110V AC Cooler, Red
- 31. NLP Libraries withNER capability Polyglot Deep Pavlov
- 32. Why for Production? FastAccurate Industry-grade maturity
- 33. example of NERusage
- 34. Same Entity MayExist with Different Spellings Interacted with Coca-Cola Company on Social Networks “Have you consumed Coca-Cola last week?”
- 35. Linking and NormalizingEntities via en.wikipedia.org/wiki/Coca-Cola en.wikipedia.org/wiki/The_Coca-Cola_Company Entity Relationship
- 36. Normalized Entities meansa Common Feature Space
- 37. Stacked Heterogeneous FeatureSpace X X ? ? X X ? ? ? ? X X X X ? ? X X X ? ? X X X Source Users Destination Users source-only entities common entities destination-only entities Latent interests Target Audience =
- 38. Common Entities translateSource to Destination Source: Social Network Panel Destination: Consumptions Survey Panel Target Audience = Common Entities ?Bayesian Model Source Target Size 1.6M / 70M = 2.3% Share of Interests
- 39. “Share of interests”encode the DNA of the Target Audience Global share of interests: 100% Common Entities Target audience share of interests: 50% 17% 50% Target Audience slice
- 40. Bayesian Model Posterior Probability ofuser belonging to projected target given the Share of Interests on common entities 𝐏( / ) =∈ 𝐏( / )∙𝐏( )∈ ∈ 𝐏( ) Evidence Prior Source Target Size=2.3%Likelihood
- 41. Evidence Decomposition 𝐏( ) Evidence 𝐏(/ )∙𝐏( )∈ ∈ 𝐏( / )∙𝐏( )∉ ∉
- 42. Marginal Positive Likelihood Binomialdistribution 𝐏( / ) ≈∈p=17%
- 43. Joint Likelihood underNaive Assumption 𝐏( , , / ) =50%17% 50% ∈ 𝐏( / )∙17% ∈ 𝐏( / )∙50% ∈ 𝐏( / )50% ∈
- 44. Destination variables TeenNick Robot Chicken Bob’s Burgers Ben & Jerry’s VenmoAngry Orchard Nintendo DSi XL Video Games Audio or Video Chat Affinity 8.9x 7.27x 2.36x 1.80x 1.62x 1.55.x 1.47x 1.45x 1.23x Predicted Probabilities provides Insights on the Projected Users PROJECTIONTarget Audience = Projected Users Probabilities Insights on Destination Variables 𝐏( / )∈
- 45. Audience Projection Ina Nutshell Social Panel Consumptions Survey PanelCommon Entities Bayesian Model Target Audience = Affinity: 1.80x Affinity: 1.55x Affinity: 1.62x
- 46. Cool! How doyou know this is accurate?
- 47.
- 48. Binary Classifier Evaluation BayesianModel Projected Users Probabilities Ground Truth Evaluation techniques ?
- 49. Validate via CommonEntities X X X X X X X X Source Users Destination Users common entities Target Audience OR= Projected Audience OR= Exact Query Replica Ground Truth
- 50. Validate via SelfReconstruction Within the Same Domain X X X X X X X X X X X X X X X X X X X Source Users Destination Users source-only entities common entities destination-only entities Target Audience = Ground Truth
- 51. Validate via Double-stepReconstruction PROJECTION PROJECTION Predicted probabilities Ground Truth
- 52. Repeat Test CasesStratifying by Category
- 53.
- 54. Golden Benchmarks Comparisonon Aggregated Insights
- 55.
- 56. Many Linked Viewsof the Same Global Population Audience Projection
- 57. Multiple Perspectives ReinforceReliability Social Panel Target Audience = Interacted with Game Informer social page Affinity: 2.17x Have you read any Game Informer issue? Affinity: 1.73x Game Informer Single Issue Magazine purchased online Affinity: 2.51x
- 58. Generalize Audience Projectionas a Domain Adaptation Problem
- 59.
- 60.
- 61.
- 62. Audience Projection adapts toany “entity domain” Bayesian Model
- 63. Accuracy and Biases canbe quantified
- 64. Strategists now have acomplete view of their Target Audience
- 65. Gianmario Spacagna Chief Scientistat Helixa.ai gspacagna@helixa.ai @gm_spacagna
- 66. Appendix A: The spaCyNER Model
- 67. Natural Language Processing(NLP) Pipeline "Mark Watney visited Mars"
- 68. The spaCy NERModel Overview EMBED ENCODE ATTEND PREDICT
- 69. Embedding Words Features token lowerprefix suffix shape Apple apple app ple Wwwww U.K. uk uk uk W.W. Fahrenheit 451 fahrenheit 451 fah 451 Wwwwwwwwww ddd Each word (token) is represented by concatenating the embeddings of all of the 4 features in order to generalize the context for unknown words.
- 70. Efficiently Embedding Words HashEmbedding reduces the dimensionality and allows to deal with large vocabularies
- 71. Encoding Sequences ofWords Residual Convolutional Neural Networks allows to encode context-independent word vectors into a context-sensitive sentence matrix. Raw tri-gram chunk Enriched tri-gram matrix Mark Watney visited “Mark Watney visited Mars”
- 72. Crafting the AttentionVector The attention vector of the trigram includes information on the encountered entities. “Mark Watney visited Mars” Attention vector Tri-gram matrix Enriched tri-gram vector
- 73. Predicting the RecognizedEntities Actions: SHIFT OUT REDUCE (Entity Tagging) Stack Buffer Segment “Mark Watney visited Mars” Actions: 1.SHIFT 2.SHIFT 3.REDUCE (PER) 4.OUT 5.SHIFT 6.REDUCE (LOC) Mark Watney Mars Mark Watney visited Mars Enriched tri-gram vector Update attention Attention vector Tri-gam matrix
- 74. Official Explanation ofspaCy NER Model https://www.youtube.com/watch?v=sqDHBH9IjRU
- 75.
- 76. Projecting the Shareof Interests on Common Entities Target Audience Projection 50% 17% 50% Share of Interests: SIZE: 60M SIZE: 200M SIZE: ? SIZE: 40M Global Audience (average american) = Target Audience evidence prior
- 77. Evidence Statistics onShare of Interests N = 180M users in U.S. population sampling rate = 1 : 10k n = 18k users in sample panel p = 17% of market penetration x = 3k expected projected users SIZE: 200M SIZE: 40M statistics: evidence
- 78. 𝐏( / )= Binomial Positive Likelihood n = 17999 x = 2999 log(p)=-5.56323 Probability of selecting 3000 / 18000 McDonald’s panel users given that the user IS part of the target∈ n = 18000 x = 3000 log(p)=-5.54342 is smaller than p=17%
- 79. 𝐏( / )= Binomial Negative Likelihood n = 17999 x = 2999 log(p)=-5.53942 Probability of selecting 3000 / 18000 McDonald’s panel users given that the user IS NOT part of the target∉ n = 18000 x = 3000 log(p)=-5.54342 p=17% is greater than