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Agenda AH systems evolution, GAF

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Presentation on theme: "Agenda AH systems evolution, GAF"— Presentation transcript:

0 Bridging Recommendation and Adaptation GAF-Twittomender compliance case-study
John Hannon, Evgeny Knutov, Paul De Bra, Mykola Pechenizkiy, Barry Smyth, Kevin McCarthy

1 Agenda AH systems evolution, GAF
AH process modelling and GAF building blocks GAF-RecSys compliance Twittomender highlights Twittomender case-study Lessons learned Further Twittomender extension / Department of Computer Science

2 AH Systems and GAF Generalize AHS functionality in GAF reference model
(incl. systems which are not labelled as AH, e.g. RecSys) Enhance GAF layered structure with the process Enrich GAF with new approaches, techniques say about system analysis, evaluation as one of the major Reference Model’s goals including those systems which are not labelled as AH systems RecSys can be viewed as being AH systems show the model and do the case-study 1990 – 1998->2002 -> now / Department of Computer Science

3 Adaptation process modeling
Classic loop user modeling - adaptation Generic Adaptivity Model diversity of adaptation process modelling approaches, representations and models GOMAWE Munich Model: Lifecycle of Adaptation / Department of Computer Science

4 Classification of AH methods and techniques; adaptation process
Classification of AH methods and techniques integrated with adaptation process Basis for the AHS layered structure original AHS classification, and adaptation process insights / Department of Computer Science

5 GAF layered model GAF aligns the order of the layers in the system according to the classification of AH methods and techniques Rotate layered structure of GAF and match with adaptation process flowcharts GAF layered structure building blocks / Department of Computer Science

6 GAF ‘sequence chart’ – RecSys compliance
present different recommendation approaches (explain an example of a hybrid recommender) / Department of Computer Science

7 Having trouble finding people to follow on Twitter?
Syncing a users account and producing followee recommendations through a range of collaborative (3) and content-based (4) strategies / Department of Computer Science

8 Twittomender architecture
Client-Server architecture Build on top of Twitter API and Lucene (full features text search engine) / Department of Computer Science

9 GAF – Twittomender compliance
/ Department of Computer Science

10 GAF – Twittomender compliance (cont.)
Resource Model – Tweets (via Twitter API) Goal Model – Twitter Profile login / Search query User Model – Twittomender User profiler Domain Model – Lucene index of users’ tweets Group Model – Collaborative twitter profile (followers) Application Model – Twittomender framework Adaptation Model – Retrieval module (Lucene) No Twittomender Contextual info / Department of Computer Science

11 Lessons Learned Helped to identify Twittomender missing parts / extension possibilities Recommendation of resources of a different nature (elaborate) User Modelling Contextualization Further Twittomender Extensions (UM interoperability) users are considered as concepts, and AH wise it is considered whether the user as a concept is suitable to follow your profile interest is matched on on the other users index of tweets, so that you get recommendations based on the user-concept suitability UM mapping from other systems without realizing the actual UM (e.g. modelling interest, etc.) !!! indicate missing parts (e.g Context of tweets, user context) – however model considers context of a different type / Department of Computer Science

12 UM interoperability in Twittomender
UM interoperability use-case (sharing Google web history and Twittomender personal user index) / Department of Computer Science

13 (to be continued) Thanks! Questions?
and Questions? / Department of Computer Science

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