modelling personalization

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I’m not you Modelling and conceptualizing personalization in information architecture Presented by / Bogo Vatovec Change Management / Knowledge Engineering / User Experience / Interaction Design / Process Engineering

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Shows approaches to modelling personalization in information architecture

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Page 1: Modelling Personalization

I’m not youModelling and conceptualizing personalization in information architecture

Presented by / Bogo Vatovec

Change Management / Knowledge Engineering / User Experience /Interaction Design / Process Engineering

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Integration of personalization in a design of an interactive system.

What will I be talking about?

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Personalization related deliverables

» Personalization Strategy

» Introduction to personalization

» Writing personalization scenarios

» Selecting the right strategy for my project

» Goals and KPIs

» Continuous Improvement program

» Conceptual models

» User, content and functional profiles

» Aspect-oriented use cases

» Personalization rules

» Probabilistic models

» Technology Selection

» Architectural requirements

» Choosing the tool

» Testing and Acceptance

I‘m going to skip the red ones….

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Introduction to personalization

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Personalization can mean lots of different things to different stakeholders

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Some of these cases are trivial

If you are happy enough that they apply to you, forget the rest of the presentation.

For the others…

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First some categorization

No pretention that this modell is complete

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Introduction – how personalization works

Personalization works essentially using the following methods:

1. Explicit settings: I want A, B and C.2. Similarity of profiles: If A is similar to B then do C.3. Sequence of actions: If 1) and 2) and 3), then do 4).4. Combination of: If A is similar to B and 1) and 2) then do 4)

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Explicit personalization – the user chooses options

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Implicit personalization

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Most popular – collaborative filtering

If user A is looking at product 1 and has a similar profile to user B and user B also bought product 1 and bought product 2, recommend product 2 to user A.

User 1 User profile 1

Content A

has

then recommend

readsUser profile 2read

Content B

Also read

Example: collaborative filtering

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Conceptualizing personalization

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Conceptualizing personalization has one basic rule

Describe behaviour without and with personalization separately.

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Separate behaviour without and with personalization

Without personalization,

the user experience looks like this…..

With personalization,

the user experience looks like that…..

Personalization expands the existing essential functionality. It is never a function on its own.

The interactive system must serve its main purpose also without personalization.

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IAs use common usage scenarios to define the context of personalization

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Make sure to consider the following

» Align personalization with the business strategy and desired user experience

» Is it important to have user profiles?

» Which data about the users is important?

» What kind of a benefit does the interactive system provide for the user and the business through different personalization types?

» Clearly state the personalization strategy by:

» Describing the personalization type used

» Describing usage scenarios illustrating the experience

» State the benefits for the business and the user

» Make sure every stakeholder understands this

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Visualizing the big picture

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Conceptual models - a „rich picture“

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Conceptual models –A relationships diagram

Behavioural trackingengine

User

experience

environment

Influences behaviour

Influences possibilities

Website

Uses

Core profile

Extended profile

Personalization engine

Content profileFills out

monitors

Fills out

Generates pages

Function profile

compares

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Profiles, profiles, profiles

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User profiles

A0: Anonymous first time visitor

A1: Anonymous returning visitor leaving traces

R1: Registered user (essential profile)

R2: Registered returning user (essential and extended profile)

RP1: Registered returning user with behaviour (essential, extended and behavioural profile)

User profile ID

Behavioral data

Core profile

Extended profile

A0

A1

R1

R2

RP1

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Content and function profile

Content/function

Attributes

Tags

Tag cloud

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Example: Detailing the user profile

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Clarify user profile management

» When is the user profile created?

» When is the user profile deleted?

» Can the user delete his profile?

» What happens to the content related to the user profile?

» How much can the user know about his profile?

» How can the user manage/modify his profile?

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It‘s time for use cases and flowcharts

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Use cases in Information Architecture

Illustrative purpose only

Learn about banking products

Open an account

User System

U1

S1

S2

U2

Screen 1

Screen 2

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Use case model allows us to better understand the interactive system

Open an account

Open a daily money account

Open a term deposit account

Open a giro account

generalize include

Open an account

Provide personal

information

Provide bank details

Provide transaction

details

Include

Include

Include

A use case model consists of three main type of relationships:

Illustrative purpose only

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The aspects-oriented use case modelling gives extend relationship a dynamic dimension

extend

Open an account

User logged in

User has an account already

Apply business rules

extend

extend

extend

Illustrative purpose only

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This dynamic dimension changes system behaviour

Open an account

User System

U1

S1

U2

Screen 3

User has an account already

extend

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User profiles can be incorporated in the activity diagram

User System/A1

U1

S1

U2

Screen 3

System/RP1

S1

S2

S3

Screen 1

Screen 2

For use case haters:Use the flow-diagramm connecting screens in the same way and create various flow for each profile-type

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This is silly.Have no idea anymore what we are talking about

Scenario Use cases Activity steps User profileConsists of Consists of Affected by

Behavioural tracking

Generated by

user

Generated by

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Bringing it all together in a readable form

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Modelling using aspects-orientation provides several advantages

» Makes conceptualization and incremental detailing easier

» Enables iterative development

» Allows for functional reduction

» Allows for easier testing and verification

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This was easy, wasn‘t it?

Wait – were are at the personalization rules?Wait – were are the Bayesian models?Wait – were are adaptive interfaces?

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Personalization rules specify conditions when personalization applies

Some examples

1. If user is looking at content A and content B has similar tags, show content B as related content.

2. If user A is looking at product 1 and has a similar profile to user B and user B also bought product 1 and bought product 2, recommend product 2 to user A.

3. If user A with profile A has click 1 then 2 then 3 and pauses for 2,5s here, show context sensitive help.

» In the conceptualizing process, we use a simple descriptive language like this.

Notice example Number 2) – it has a sequence of conditions…. Will talk about this in a minute.

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Let‘s move to technology a bit and then explore the rest

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Personalization strategy defines the use cases for technology

» Capture user behaviour on the website and store it in the user profile.

» Dynamically generate content recommendations based on user behaviour in real time (in one session)

» Dynamically generate content recommendations based on the stored user profile.

» Provide for statistical analysis of user behaviour based on user profiles to expand marketing knowledge.

» Provide the user the possibilities to customize the layout of the home pages.

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To come up with requirements, you need to further specify them

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Technical architecture for personalization

Users

User interface Layer Personalization

Profile Layer Specific Values

Vocabulary Layer Attributes

Content

User Profile

Content Profile

Content Attributes

User Attributes

Personalization Rules

Personalization Rules

Modified from the model by Argus Center for Information Architecture

AnalyticsUser

BehaviourContent Statistics

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How can this model help us?

» How much does one system require explicit user profile setting?

» How much implicit?

» How does it integrate with other systems to fill out the profiles?

» How does the system support vocabulary level personalization rules?

» How easy it is to automatically populate content values?

» What features does the personalization system include to manage controlled vocabularies?

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Those of you who carefully listened notices that I missed an important compontent…..

TIME

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Until now we looked at the user and content profiles as one object with one state

Open an account

User System

U1

S1

U2

User profileFills out

Fills out

Fills out

Since the user profile has only one state, we forget the sequence of actions – user‘s decision making process

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Here the fun begins: Adaptive user interfaces and even – yes – artificial intelligence

Adaptive user interfaces are interfaces which automatically acquire knowledge about the users, update this knowledge over time, and uses this knowledge to adapt to the users’ requirements.

Artificial intelligence is defined as as "the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.

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Remember the personalization rule from before

If user A with profile A has click 1 then 2 then 3 and pauses for 2,5s here, show context sensitive help.

To model this, we need to have a user profile with a sequence of events

Open an account

User System

U1

S1

U2

User profile 1'

Fills out

Fills out

Fills out

User profile 1'’

User profile 1'’’

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» How can we track all past user actions? Which data do we need to track?

» Can previous behaviour give us enough information for action?

» How can we provide qualitative personalization rules based on this data?

» Can previous behaviour provide us enough information to predict future user actions?

A Wizard of Oz research methodology in reverse – can the experts predict user action?

We talk about probability, game theory and statistics…

Considering time brings a new dimension of complexity and challenges

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This is where probability theories and machine learning come into the game

The good news is that – unless you have a peculiar interest in this – you can get along with the first part of the presentation.

http://en.wikipedia.org/wiki/Machine_learning

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Summary – remember what we talked about…

» We looked at the following topics

» Personalization types and categorization

» Personalization scenarios

» User, content and functional profiles

» Aspect-oriented use cases

» Personalization rules

» Probabilistic models

» Architectural requirements

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Summary – the doggy bag

» What you should take with you

» Personalization was not invented by Amazon.

» Define what personalization is for you

» Explain this to every stakeholder

» Model personalization so that project members understand how it will work

» Use various models for various stakeholders

» Explore various modelling methods and start using them. They help in the communication and clarification of issues and open points.

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Thank you!

Bogo Vatovecbovacon

Boxhagener Str. 111 / 10245 BerlinT +49 30 20078666 / F +49 30 20078661 / [email protected] / www.bovacon.com

© bovacon