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CSA4080: Adaptive Hypertext Systems II

CSA4080: Adaptive Hypertext Systems II

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CSA4080: Adaptive Hypertext Systems II

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  1. CSA4080:Adaptive Hypertext Systems II Topic 2: User-Adaptive Systems Dr. Christopher Staff Department of Computer Science & AI University of Malta 1 of 20 cstaff@cs.um.edu.mt

  2. Aims and Objectives • To consider where AHSs fit in the realm of User-Adaptive Systems • To describe other systems that adapt to the user 2 of 20 cstaff@cs.um.edu.mt

  3. User-Adaptive Systems • Systems that adapt to their environment are called Adaptive Systems • e.g., Artificial Life • Systems that adapt to their users are called User-Adaptive Systems • e.g., Adaptive User Interfaces, Recommender Systems, Reconnaissance Agents, Adaptive Information Retrieval, User modelling, personal assistants, personalisation, information filtering, ambient intelligence... 3 of 20 cstaff@cs.um.edu.mt

  4. User-adaptive systems: Functions • Help user to find information • Recommend products to user • Tailor information presentation to user • Help user to learn about a topic • Help user to use a system • Adapt an interface to user • Perform routine tasks on behalf of user • Support collaboration between user and other persons ijcai01-tutorial-jameson.pdf 4 of 20 cstaff@cs.um.edu.mt

  5. Typical Properties:Adaptive User Interfaces • As Graphical User Interfaces become more complex, users need more help with the interface • Adaptive user interfaces learn a user model by tracing the interactions with the interface • They learn to improve their ability to interact with a user adapt.um99.pdf 5 of 20 cstaff@cs.um.edu.mt

  6. Typical Properties:Adaptive User Interfaces • Examples of AUIs are: • Recommendation systems, Syskill & Webert • Personalisation systems, Calendar Apprentice • Content-based filtering, NewsWeeder 6 of 20 cstaff@cs.um.edu.mt

  7. Typical Properties:Adaptive User Interfaces • AUIs concentrate on how the user model is learnt • So concentrates on the user interaction, and hence the interface between user and machine 7 of 20 cstaff@cs.um.edu.mt

  8. Typical Properties:Reconnaissance Agents • E.g., Letizia, PowerScout (Why-Surf-Alone.pdf) • Reconnaissance agents: “programs that look ahead in the user’s browsing activities and act as an advance scout to save the user needless searching and recommend the best paths to follow.” Why-Surf-Alone.pdf 8 of 20 cstaff@cs.um.edu.mt

  9. Typical Properties:Reconnaissance Agents • Provide local and/or global guidance • Typically, less user involvement in identifying interest is better • E.g., search engine usually requires active role • Reconnaissance agent observes user to learn model 9 of 20 cstaff@cs.um.edu.mt

  10. Typical Properties:Adaptive Information Retrieval • Can bridge vocabulary ‘gap’ by learning associations between user and document vocabulary • Can rephrase user query based on user interactions with docs in results set • Can provide ‘context’ for user terms to disambiguate terms 10 of 20 cstaff@cs.um.edu.mt

  11. Typical Properties:Recommender Systems • E.g., IMDB, Amazon, ... • Two main types, but with same aim • Collaborative vs. content-based • Aim to make recommendation to individual, based on past events recommender 0329_050103.pdf 11 of 20 cstaff@cs.um.edu.mt

  12. Typical Properties:Recommender Systems • Collaborative • System will make recommendation based on what other similar user have done • User similarity: demographic vs. interaction history • Uses ratings • If X & Y gave similar ratings for A, C, D, then recommend F to Y if X liked F 12 of 20 cstaff@cs.um.edu.mt

  13. Typical Properties:Recommender Systems • Content-based • Also uses ratings, but we recommend F to Y, if Y gave high rating to items in , where  is set of objects similar to F • Collaborative recommender systems suffer if item is unrated • Content-based systems suffer is user has no history 13 of 20 cstaff@cs.um.edu.mt

  14. Typical Properties:Personal Assistants • User delegates work to the computer • Find and filter information • Customise views of information • "They enable users to center their interactions at the content level (semantics), partially removing syntactic difficulties. They also enable users to index (contextualize) content to specific situations that they understand better (pragmatism)" Boy, Guy A. (1997) Software Agents for Cooperative Learning. In Software Agents , MIT Press (1997) 14 of 20 cstaff@cs.um.edu.mt

  15. Typical Properties:Personalisation • Changing view/interface/content to needs and requirements of user • Can apply to anything 15 of 20 cstaff@cs.um.edu.mt

  16. Typical Properties:Information Filtering • Inverse function of information retrieval • Constant stream of changing information (e.g., a news wire) where each item needs to be sent to an interested user • Wrong item to user, user becomes overloaded • Item not sent to user, user misses information 16 of 20 cstaff@cs.um.edu.mt

  17. Typical Properties:Ambient Intelligence • Devices that adapt to changes in their and their user’s environment 17 of 20 cstaff@cs.um.edu.mt

  18. Conclusions • So, there is lots of overlap in the different fields of user-adaptive systems • We haven’t talked about adaptive hypertext systems, intelligent tutoring systems, on-line help systems, ... 18 of 20 cstaff@cs.um.edu.mt

  19. Conclusions • What properties of the user should be modelled? • What input data about the user should be obtained? • What techniques should be employed to make inferences about the user? • What functions are to be served by the adaptation? • How should decisions about appropriate adaptive system behaviour be made? • What empirical studies should be conducted? ijcai01-tutorial-jameson 19 of 20 cstaff@cs.um.edu.mt

  20. For next lecture • Read http://ted.hyperland.com/buyin.txt 20 of 20 cstaff@cs.um.edu.mt