User Models for Personalization Josh Alspector Chief Technology Officer

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 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 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 Marketing Market vs. Customer Share 1to1 Marketing Customer Needs Satisfied Customers Reached

E-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

Personalization Deliver 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 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 buy Nothing is more valuable in the information age

Benefits for Customers Reduce search time & effort Improve recommendations –reduce cost, increase satisfaction Improve over time through learning Tailored content and advertising One-to-one marketing Build communities

Benefits 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 Models Simulations –Understand properties Controlled experiments –Focus groups –Friendly users Field studies –Use actual marketplace

Group 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 & 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: 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 Movies

Group vs. Individual: Movies User IDLinear:comb. features Clique:rank distance U U U U U Avg. Correlation.38.58

Data Analysis: NewSense “Bag of words” for visited headlines –stemming, stop words Score recent words higher Similarity measure –cosine (query, document) word vectors “Query” based on visited documents –terms in relevant (visited) - factor*terms in irrelevant (not visited) documents

Evaluation 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: News

Simulation study (Ariely, MIT) Create “people” Create products Create decision rule Create “markets” with smart agents

Group & Individual results I Constant taste Time Recommendation Quality

Group & Individual results II Gradual taste Change Time Recommendation Quality

Group & Individual results III Abrupt taste Change Time Recommendation Quality

Group & Individual results IV New Product I Time Adoption % New product introduction

Group & Individual results IV New Product II Time Recommendation Quality New product introduction

Conclusion 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