Modelling Personalization

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

Integration of personalization in a design of an interactive system.
What will I be talking about?

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….

Introduction to personalization

Personalization can mean lots of different things to different stakeholders

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…

First some categorization No pretention that this modell is complete

Introduction – how personalization works
Personalization works essentially using the following methods:
Explicit settings: I want A, B and C.
Similarity of profiles: If A is similar to B then do C.
Sequence of actions: If 1) and 2) and 3), then do 4).
Combination of: If A is similar to B and 1) and 2) then do 4)

Explicit personalization – the user chooses options

Implicit personalization

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.
Example: collaborative filtering

Conceptualizing personalization

Conceptualizing personalization has one basic rule Describe behaviour without and with personalization separately.

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.

IAs use common usage scenarios to define the context of personalization

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
Make sure to consider the following

Visualizing the big picture

Conceptual models - a „rich picture“

Conceptual models – A relationships diagram

Profiles, profiles, profiles

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

Content and function profile Content/function Attributes Tags Tag cloud

Example: Detailing the user profile

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?

It‘s time for use cases and flowcharts

Use cases in Information Architecture Illustrative purpose only

Use case model allows us to better understand the interactive system generalize include A use case model consists of three main type of relationships: Illustrative purpose only

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

This dynamic dimension changes system behaviour

User profiles can be incorporated in the activity diagram For use case haters: Use the flow-diagramm connecting screens in the same way and create various flow for each profile-type

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

Bringing it all together in a readable form

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

This was easy, wasn‘t it? Wait – were are at the personalization rules? Wait – were are the Bayesian models? Wait – were are adaptive interfaces?

Personalization rules specify conditions when personalization applies
Some examples
If user is looking at content A and content B has similar tags, show content B as related content.
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.
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.

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

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.

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

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 Analytics User Behaviour Content Statistics

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?

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

Until now we looked at the user and content profiles as one object with one state Since the user profile has only one state, we forget the sequence of actions – user‘s decision making process

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.

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

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

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

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

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.

Thank you! Bogo Vatovec bovacon Boxhagener Str. 111 / 10245 Berlin T +49 30 20078666 / F +49 30 20078661 / office@bovacon.com / www.bovacon.com © bovacon

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Modelling Personalization

by Bogo Vatovec on Oct 02, 2008

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Modelling Personalization — Presentation Transcript