user models for personalization josh alspector chief technology officer
TRANSCRIPT
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User Models for PersonalizationUser Models for Personalization
Josh Alspector
Chief Technology Officer
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One-to-One Marketing One-to-One Marketing
Peppers & Rogers Customized products, services for
individual customers Market knowledge from observations,
dialogue and feedback with individuals Focus on customer loyalty Customer Relationship Management
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Technical HeritageTechnical Heritage
• Customer databases: remember this specific customer
• Interactivity: customer talks to us or acts
• Mass customization: make or do something for him
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Loyalty: A Learned RelationshipLoyalty: A Learned Relationship
Customer tells you what he wants You tailor your product, service or
elements associated with it The more effort the customer invests, the
greater their stake in product or service Now the customer finds it more
convenient to remain loyal rather than re-teach a competitor
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Traditional MarketingTraditional Marketing
Market vs. Customer Share
1to1 Marketing1to1 Marketing
Customer Needs Satisfied
Customers Reached
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E-Commerce ChoicesE-Commerce Choices
If you operate in the product dimension– Then you must be the lowest cost producer– Buy a new car at $25 over invoice
Or, operate in the customer dimension– Remember this customer when he comes
back–Make it easier and easier to do business
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PersonalizationPersonalizationDeliver customized offerings– Create products from components– Configure and deliver to personal taste
Generate recommendations– Analyze user data– Recognize patterns of behavior– Develop adaptive models of users
Retain customers– Identify and understand individuals– Match products with needs
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User ModelUser Model
Ideally a model of the user’s mind– allows perfect prediction of user’s needs
for news and entertainment– allows advertisers to create ads user will
always click on– allows vendors to present products a user
will always buyNothing is more valuable in the
information age
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Benefits for CustomersBenefits for Customers
Reduce search time & effortImprove recommendations– reduce cost, increase satisfaction
Improve over time through learningTailored content and advertisingOne-to-one marketingBuild communities
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Benefits for ProvidersBenefits for Providers
Match customer needs– Convert browsers to buyers– 80% of orders come from 20% of audience
Higher customer loyalty & satisfaction Continuous improvement from learning Continuous high-quality market research
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How to Study User ModelsHow to Study User Models
Simulations– Understand properties
Controlled experiments– Focus groups– Friendly users
Field studies– Use actual marketplace
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Group Models: Fill-in ProfilesGroup Models: Fill-in Profiles
Usually a registration procedure– income, education, sex, age, zip code– sports, hobbies, entertainment, news– understanding: demographics used by
vendors in exchange for access to site– basis for most targeted ads– interests don’t fall into categories, are
hard to articulate, miss users’ richness
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Group Models:Cliques & ClicksGroup Models:Cliques & Clicks Clique-based classifiers– ‘collaborative filtering’ looks at users with
similar tastes to predict choices– Amazon: suggest books based on your order,
richer than category ‘romance’
Clickstream analysis – high reach– Polluted data from random clicking
% of Audience with Clickstream Data
% of Audience with Registration Data
% of Audience with Transaction Data
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Individual Models: FeaturesIndividual Models: Features
Feature-based classifiers– multiple attributes considered– compared both for movies
Text-based classifiers– information retrieval: word vector space– cluster documents with similar words– NewSense displays precision of 75%– most internet information is text– no need to fill in form or rate products
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Individual Model for MoviesIndividual Model for Movies
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Group vs. Individual: MoviesGroup vs. Individual: Movies
User ID Linear:comb. features
Clique:rank distance
U21 .36 .67
U111 .53 .84
U39 .54 .75
U145 .31 .31
U77 .37 .34
Avg. Correlation
.38 .58
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Data Analysis: NewSenseData Analysis: NewSense
“Bag of words” for visited headlines– stemming, stop words
Score recent words higherSimilarity measure– cosine (query, document) word vectors
“Query” based on visited documents– terms in relevant (visited) - factor*terms
in irrelevant (not visited) documents
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Evaluation of DataEvaluation of Data
Precision: well-defined– visited&relevant/all visited
Recall: ill-defined here– visited&relevant/all&relevant
Use average precision– weighted by threshold of relevancy
Rocchio, Bayes, SVM: P=0.75
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Individual Model: NewsIndividual Model: News
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Simulation study (Ariely, MIT)Simulation study (Ariely, MIT)
Create “people” Create productsCreate decision ruleCreate “markets” with smart
agents
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Group & Individual results IGroup & Individual results I
Constant taste
Time
RecommendationQuality
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Group & Individual results IIGroup & Individual results II
Gradual taste Change
Time
RecommendationQuality
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Group & Individual results IIIGroup & Individual results III
Abrupt taste Change
Time
RecommendationQuality
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Group & Individual results IVGroup & Individual results IV
New Product I
Time
Adoption %
New product introduction
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Group & Individual results IVGroup & Individual results IV
New Product II
Time
RecommendationQuality
New product introduction
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ConclusionConclusion
Wide variety of user models with different analyses, applicability & effectiveness
Group models can “jump start” from zero knowledge
Individual adaptive models are better over the long-run and for new products