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Ah.hypermedia gaf.poster

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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.

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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

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Ah.hypermedia gaf.poster

  • 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