Personalized Hypermedia Presentation Techniques for Improving

Online Customer Relationships

Alfred Kobsa, Jurgen Koenemann and Wolfgang Pohl

Presented by Lei Zan, Amy Henckel

Outline

Why personalized systems (an example)What input to personalized systemsHow to acquire dataHow to represent and inferHow to produce adaptationConclusions

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

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

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

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

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

One example: AVANTI

You enter the welcome page and login, in order to allow system recognize you.

One example: AVANTI

You are asked to fill in a questionnaire to get information to tailer to your specific need.

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.

One example: AVANTI

Your first question is how to reach Siena from Roma. You find train route from Roma to Siena.

One example: AVANTI

If you are interested in churches, you are presented a list of churches by selecting appropriate options.

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.

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.

One example: AVANTI

You log in and the system remembers you and welcome you in Siena AGAIN.

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.

One example: AVANTI

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

Outline

Why personalized systemsWhat input to personalized systemsHow to acquire dataHow to represent and inferHow to produce adaptationConclusions & discussions

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

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

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)

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

What are inputs to personalized systems

Usage data: interaction behaviourObservable 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

What are inputs to personalized systems

Usage dataUsage 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

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 )

Outline

Why personalized systemsWhat input to personalized systemsHow to acquire data How to represent and inferHow to produce adaptationConclusions & discussions

How to acquire data

User modelCollection of explicit assumptions about user data

Usage modelConstruct aggregated information about a user’s

interactive behaviour from observations

Environment model

How to acquire data

User model acquisition methodsActive 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

How to acquire data

User model acquisition methodsPassive 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

How to acquire data

User model acquisition methodsPassive 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

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

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

Outline

Why personalized systemsWhat to input to personalized systemsHow to acquire data How to represent and inferHow to produce adaptationConclusions & discussions

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)

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

How to represent and infer

A concept hierarchy in animal kingdom

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.