User Modeling, Adaptation,

PersonalizationPart 2

ΕΠΛ 435:Αλληλεπίδραση

Ανθρώπου Υπολογιστή

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Schema of User-Adaptive Systems

USER MODEL

USER MODELACQUISITION

USER MODELAPPLICATION

INFORMATION ABOUT U ADAPTING TO U

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Two steps

Content adaptation – what content is most appropriate for the current user based on the user model

Content presentation – how to most effectively present the selected content to the user

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Page-based approaches

Pre-defined pagesThe adaptation mechanism selects the most

appropriate page

UM

Select pagesShow to user

Advantages and disadvantages?01/11/2013

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Example: KBS Hyperbook

•Adaptive Information Resources

•Adaptive Navigational Structure

•Adaptive Trail Generation

•Adaptive Project Selection

•Adaptive Goal Selection http://wwwis.win.tue.nl/asum99/henze/henze.html01/11/2013

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Example: AHA

•Navigation frame (generated by the system)

•Content frame – combines fragments prepared by authors

• Inclusion/exclusion of links;

•Inclusion/exclusion of detail

http://aha.win.tue.nl/01/11/2013

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Dynamic approaches

• Content adaptation:– Dynamic selection of content– Dynamic structuring of the content

• Content presentation– Defining relevance and focus– Dynamic media adaptation

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Dynamic content adaptation

• Content automatically selected from:– Knowledge base, relevance measures

(e.g. ILEX, STOP)– Bayesian networks expressing causal probabilistic

relationships between variables from the domain (e.g. NAG)– User preferences model, importance measures

(e.g. GEA, RIA)• Content automatically structured:

– Task- accomplished planners– Argumentation models– Conversation theories

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Example: ILEXhttp://www.hcrc.ed.ac.uk/ilex/

•Domain Model

•The Content Potential•Text Structure•Syntactic Structure

•Presentational Forms

•Representation of Context

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Example: GEA(Carenini & Moore, 2001)

•User preferences in a hierarchical model (e.g. house, location, number of bedrooms)

•Argument structure tailored to user preferences (uses measure of relevance)

•Level of detail will differ for users or for the same user at different stages

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Example: RIAhttp://www.research.ibm.com/RIA/

Two different responses to the same query depending on user preferences

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Dynamic content presentation

Maintaining focus and context

• Focus – emphasise the content that has been found most relevant to the user

• Context – allow access to less relevant content to preserve context – Stretch text– Scaling fragments– Dimming fragments– Summary thumbnail

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Follows the “fish eye” visualisation Technique

Adaptation of an online guide about cultural events in Toronto: http://whatsuptoronto.com/

Example: scaling approach

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Dynamic content presentation

Media adaptation: factors

• User-specific features• Information-specific features• Contextual information• Media constraints• Limitations of technical resources

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Dynamic content presentation

Media adaptation: approaches

• Rule-based approaches– Using rules to define how to take into account the

media factors in media selection

• Optimisation approaches– Given the media factors, find the media

combination that produces the most optimal result

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Example: RIA

Optimisation adaptationhttp://www.research.ibm.com/RIA/

The optimization procedure deals with: (1) suitability of the information to the media; (2) increase recallability; (3) maintain

presentation consistency01/11/2013

Recommender systems: intro

The problem: too much content! too many choices!

Recommendation Features

How do recommender systems work?

Major types of algorithmsCollaborative or social filtering

Suggestion lists, “top-n” offers and promotionsContent-based recommenders

E-mail filters, clipping services

Hybrid recommenders Suggestion lists, “top-n” offers and promotions

Collaborative filtering

Other’s ratings What others like

My ratings What I think like

Give me what people similar

to me would like“Word of mouth”

“Voting”

Content-based Filtering

Content Appropriate information about it

User profile Relevant to the content

Give me only those I like

User Profile

Content

Hybrid Filtering

Combining both Building on advantagesOvercoming limitations

User ProfileContent

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Recommendations/Similarities

Similar friends (left) and recommended pages (right) based on user similarity

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What do you think Amazon is using?

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