The document presents an adaptation model for adaptive hypermedia systems. The model includes three main layers: a runtime layer for presenting hypertext and user interactions, a storage layer containing a network of nodes and links in a database, and a within-component layer for the content and structure inside nodes. The within-component layer handles adaptation using rule systems, user modeling, domain modeling, and other techniques.

1. Adaptation Model
Runtime Layer
(presentation of hypertext, user
interact.)
Presentation Specification
Storage Layer
(a database containing a
network of nodes and links)
Anchoring
Within Component Layer
(the content/structure inside the
nodes)
- adaptation (rule
sysrtem)
- user modelling
- domain
modelling
- search
- group adaptation
- higher order adaptation
- enhanced reasoning
- data mining support
- open corpus
- user/usage context
- semantic web / ontologies
Run-time Layer
Presentation
Specification
Within-component layer
Anchoring
Domain
Model
User
Model
Storage
Layer
Application Model
Presentation Model
Resource Layer
Domain
Model
User
Model
Storage
Layer
Context
Model
Adaptation Model (AE)
Group
Model
HOA
Dexter Model AHAM GAF
E. Knutov, P. De Bra, M. Pechenizkiy
Eindhoven University of Technology
e-mail: e.knutov@tue.nl
http://www.win.tue.nl/~eknutov/gaf.html
Adaptive Hypermedia Systems evolution,
from Dexter and AHAM to GAF
GoalModel
DomainModel
UserModel
ContextModel
ResourceModel
ApplicationModel
GroupModel
AdaptationModel
PresentationModel
HigherOrderAdaptation
UserContext
UsageContext
requesting recommendation
Group
formation
Defining the
usage context
Acquiring user
context
UM-DM
overlay
sustainability
Acquiring
resources for
the concepts
Defining
user goal
Aligning goal with
the Domain
Considering
usage context
Passing
content to be
rendered and
presented
Maintaining
and
Acquiring
Adaptive rules
of higher order
Initiating
adaptation
Invoking
adaptation
Methods and
techniques
Retrieving/updating
UM state
Maintaining and
updating HOA with
system usage data
Retrieving concept
corresponding domain
information (hierarchy,
resources, meta data)
Crawler
Ratings
User – stating
the query
User Profile
IP, profile, etc.
Item
dataset
Recommend.
history
User Interface:
Presenting
recommendations,
ranking info., etc.
(snippets, etc.)
Search
Engine
Search Query
Interpreting
Query
(refolmulation,
extern lex., etc.)
applying
collaborative
filtering
Maintaining
collaborative
profile
AHS
Overlay
Model
AH
methods and
techniques
Collaborative
Filtering
Collaborative
Profile
Itemset
Content-based
Filtering
Context
Recommendations
Itemsets
Recommender
Ranking
Recommender
Hybridization
(if required)
User
Ratings
Itemset
Ratings
Item-User
filtering
(coll. content
based)
Filtering
Ranking
(search)
Knowledge-based
Filtering
content (feature
space) filtering
Explicit
recommendatio
n query
(formulated by
the user)
[e.g. for KB
recom.]
CBF req.
Coll.F req.
view AH view
- manual, statically
defined view
- no personalization
- dynamic access, based on the
UM state
- personalized content retrieval
Versioning
repository
AH personalization UM
maintains
personalized
versions of
information
Bridging Versioning and AH
Event
is because of
is used in
leads to
happens in
occurs at
is involved in
location
timestamp
agent
rationale
device
action
Where
When
Who
Why
Which
How
Bridging Provenance and AH
making recommendations,
adapting presentation
of search results, etc.
adapting the
search query
(reformulation,
extern lexicon, etc.)
Front-end
process
Back-end
process
WWW
Web
crawler
Indexer
User
Interface
Ranking
Query
parser
Indexes
search
adaptive
functionality
conventional
web search
functionality
Legend:
user
model
personalized
index
personalized UI
(presentation.navigation, etc.)
query/result
adapted: query input
adaptive
crawling
Adaptive Search
Project number: 612.061.612
index
www
The Generic Adaptation Framework research project aims to develop a new
reference model for the adaptive hypermedia research field. The new model
will consider new developments, techniques and methodologies in the areas
of adaptive hypermedia and adjacent fields (Data Mining and Machine
Learning, Semantic Web, Open Corpus Adaptation, etc.).
Introduction, Goal