Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships

1. Alfred Kobsa, Jurgen Koenemann and Wolfgang Pohl Presented by Lei Zan, Amy Henckel Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships

2. Outline Why personalized systems (an example) What input to personalized systems How to acquire data How to represent and infer How to produce adaptation Conclusions

3. Introduction Personalization, micro-marketing, one-to-one marketing Provide values to customers by serving them as individuals Improve customer relationship, turn web visitors to customers Web provides a platform to realize this business model It facilitates large amount of data collection It supports dynamic creation of content/presentation It enables global presence

4. Introduction • Personalized hypermedia application • Adapts the content, structure, and/or presentation of the networked hypermedia objects to • Each individual user’s characteristics, usage behaviour, and/or usage environment • Adaptability and adaptivity • Adaptability: the user is in control of adaptation steps • Adaptivity: the system performs all adaptation steps automatically • Adaptability and adaptivity coexist

5. Introduction • Personalization process includes • Acquisition • Identify info. about user characteristics, usage behaviour and environment • Make this info. accessible to adaptation component • Construct user/usage/environment model • Representation and secondary inference • Express content of user/usage models appropriately • Draw further assumptions about users, their behaviour & environment • Production • Generate adaptation, given a user/usage/environment model

6. One example: AVANTI • Background • A project (1996-1998) funded by the European Commission • Tourist information system: assist travel planning, e.g. Transportation, accommodation, day-to-day activities • Adaptation is applied at both user interface, content level

7. One example: AVANTI • Demonstration • Scenario: You are a student in Roma who studies history of art decides to go to Siena for one week to study the culture there. You are suggested to use AVANTI system to get information for your trip

8. One example: AVANTI • You enter the welcome page and login, in order to allow system recognize you.

9. One example: AVANTI • You are asked to fill in a questionnaire to get information to tailer to your specific need.

10. One example: AVANTI • The system load a new page. For new users, a dialog box informs that the page has been loaded to avoid confusion.

11. One example: AVANTI • Your first question is how to reach Siena from Roma.You find train route from Roma to Siena.

12. One example: AVANTI • If you are interested in churches, you are presented a list of churches by selecting appropriate options.

13. One example: AVANTI • A result of adaptivity: after picking one church, check route and working hours, etc, the system recognize you are interested in churches and list other church’ info as options for you.

14. One example: AVANTI • Three months later, you decide to go back to Siena again. • In the meantime, you have attended a course to learn how to use a computer. • Moreover, you have used many other times the AVANTI system.

15. One example: AVANTI • You log in and the system remembers you and welcome you in Siena AGAIN.

16. One example: AVANTI • Interface Adaptivity: a list of links in the left side; no feedback dialog box; you are considered as an expert user now.

17. One example: AVANTI • A result of adaptivity: shortcuts and additional navigation support for quick access are provided, as you are recognized as expert.

18. Outline Why personalized systems What input to personalized systems How to acquire data How to represent and infer How to produce adaptation Conclusions & discussions

19. What are inputs to personalized systems User data Info. about user characteristics Usage data User’s interactive behaviour Environment data (of user) Software Hardware Physical environment

20. What are inputs to personalized systems User data Demographic data Record data (e.g. name, address, phone numbers) Geographic data (e.g. area code, city, state) User characteristics (e.g. age, sex, education) Registration for information offerings Note: today’s personalized system contains mainly those demographic data and purchase data. It has high value when combined with high-quality statistical data, e.g. purchase behaviour of different user groups

21. What are inputs to personalized systems User data User knowledge (about concepts, relationships between concepts in an application domain) e.g. Generate expertise-dependent product description User skills and capabilities e.g. Adaptive help messages for UNIX commands e.g. AVANTI takes the needs of disabled people (wheel-chaired, vision-impaired)

22. What are inputs to personalized systems • User data • User interests and preferences • e.g. Sell cars to different customers emphasizing different attributes (speed, safety, etc) • User goals and plans • Find information on a certain topic, or shop for some products • Support users to achieve their goals • e.g. Present to users only information relevant to their goals

23. What are inputs to personalized systems Usage data: interaction behaviour Observable data Selective actions Indicator of user’s interest, or unfamiliarity, or preferences Viewing time Potential indicator of user interest Ratings Indicate how relevant or interesting the object is e.g. eBay, Amazon Purchases and purchase-related actions Strong indicator of user interest

24. What are inputs to personalized systems • Usage data • Usage regularities: further processing of data • Usage frequency • e.g. AVANTI monitors how often individual users visit certain pages and introduces shortcut links • Situation-action correlations • e.g. Email assistant: suggest how to deal with incoming emails, based on statistics of correlations between previous emails (situations) and how user processed them (actions) • Action sequences • Used to recommend macros for frequently used action sequences, predict future actions

25. What are inputs to personalized systems • Environment data: impact web usage • Software environment • Brower version and platform, availability of plug-ins, java and javascripts • Hardware environment • Bandwidth, processing speed, display devices, input devices • locale • Users’ location, characteristics of locale (e.g. noise level )

26. Outline Why personalized systems What input to personalized systems How to acquire data How to represent and infer How to produce adaptation Conclusions & discussions

27. How to acquire data User model Collection of explicit assumptions about user data Usage model Construct aggregated information about a user’s interactive behaviour from observations Environment model

28. How to acquire data User model acquisition methods Active acquisition: User-supplied information Questionnaires, initial interviews e.g. AVANTI welcome page asks questions (computers, AVANTI systems, about disabilities) Downside: paradox of the active user User wants to get started immediately and get work done soon Time is saved in the long term by taking initial time to optimize system

29. How to acquire data • User model acquisition methods • Passive acquisition • Acquisition rules • Refer to observed user actions or straightforward interpretation of user behaviour • e.g. a classic domain-independent rule: “If the user wants to know X, then the user does not know X” • Plan recognition • Recognize user’s goal from observed user interactions • Suitable for applications with a small number of goals and ways to achieve the goals

30. How to acquire data • User model acquisition methods • Passive acquisition • Stereotype reasoning • Categorize and associate a stereotype with each category • Stereotype contains standard assumptions about members of that category and activation conditions • Evaluate activation conditions, apply content of stereotype as assumptions to the particular user • e.g. if the user is interested in childcare, activate “parent” stereotype

31. How to acquire data Usage model acquisition methods Simple technique Record user actions in order to obtain information about user behaviour Learning algorithms Memory-based learning, reinforcement learning, induction of decision tree e.g. learn situation-action correlations; these data are used to predict user behaviour in future situations

32. How to acquire data • Environment data acquisition methods • Software environment: http header • Hardware environment • Difficult to assess • e.g. AVANTI evaluates bandwidth from media download time • Locale • Location can be recorded in database or use GPS

33. Outline Why personalized systems What to input to personalized systems How to acquire data How to represent and infer How to produce adaptation Conclusions & discussions

34. How to represent and infer Why need representation and inference Some applications operate directly on results of user/usage/environment model Some applications need user/usage model representation and further inference Deductive reasoning (from general to specific) Inductive reasoning (from specific to general)

35. How to represent and infer • Deductive reasoning (from general to specific) • Logic-based representation and inference • e.g. Concept formalism: form user knowledge base • Shortcomings of logic-based approaches • Limited ability to deal with uncertainty and with changes to the user model • Representation and reasoning with uncertainty • Bayesian network, evidence-based, fuzzy logic approach for probabilistic user model representation

36. How to represent and infer • A concept hierarchy in animal kingdom

37. How to represent and infer • Inductive reasoning (from specific to general): • Learning about the users: monitor users’ interaction with system and draw general conclusions based on observations • Learning is used to construct “interest profiles” • Interest profiles represent a user’s interest in an object, based on an assessment of his interest in specific features of the object • e.g. assumption of user interest in movies is determined by preferences about actor, director and other movie features • Neural network, machine learning, nearest-neighbour algorithm, induction of decision trees, etc.