User Models for Personalization

1. User Models for Personalization Josh Alspector Chief Technology Officer

2. 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

3. Technical Heritage • Customer databases:remember this specificcustomer • Interactivity:customer talks to us or acts • Mass customization:make or do something for him

4. 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

5. 1to1 Marketing Market vs. Customer Share Customer Needs Satisfied Traditional Marketing Customers Reached

6. 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

7. 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

8. 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

9. 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

10. 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

11. How to Study User Models • Simulations • Understand properties • Controlled experiments • Focus groups • Friendly users • Field studies • Use actual marketplace

12. 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

13. 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

14. 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

15. Individual Model for Movies

16. Group vs. Individual: Movies

17. 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

18. 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

19. Individual Model: News

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

21. Group & Individual results I • Constant taste Recommendation Quality Time

22. Group & Individual results II • Gradual taste Change Recommendation Quality Time

23. Group & Individual results III • Abrupt taste Change Recommendation Quality Time

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

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

26. 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