User Models for Personalization Josh Alspector Chief Technology Officer

User Models for PersonalizationUser Models for Personalization

Josh Alspector

Chief Technology Officer

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

Technical HeritageTechnical Heritage

• Customer databases: remember this specific customer

• Interactivity: customer talks to us or acts

• Mass customization: make or do something for him

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

Traditional MarketingTraditional Marketing

Market vs. Customer Share

1to1 Marketing1to1 Marketing

Customer Needs Satisfied

Customers Reached

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

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

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

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

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

How to Study User ModelsHow to Study User Models

Simulations– Understand properties

Controlled experiments– Focus groups– Friendly users

Field studies– Use actual marketplace

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

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

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

Individual Model for MoviesIndividual Model for Movies

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

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

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

Individual Model: NewsIndividual Model: News

Simulation study (Ariely, MIT)Simulation study (Ariely, MIT)

Create “people” Create productsCreate decision ruleCreate “markets” with smart

agents

Group & Individual results IGroup & Individual results I

Constant taste

Time

RecommendationQuality

Group & Individual results IIGroup & Individual results II

Gradual taste Change

Time

RecommendationQuality

Group & Individual results IIIGroup & Individual results III

Abrupt taste Change

Time

RecommendationQuality

Group & Individual results IVGroup & Individual results IV

New Product I

Time

Adoption %

New product introduction

Group & Individual results IVGroup & Individual results IV

New Product II

Time

RecommendationQuality

New product introduction

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