CSA3212: User-Adaptive Systems

1. Dr. Christopher Staff Dept. Intelligent Computer Systems University of Malta CSA3212:User-Adaptive Systems Topic 2: User-Adaptive Systems

2. Aims and Objectives To describe systems that adapt to the user To describe typical problems that AHSes try to solve.

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...

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

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

6. Typical Properties:Adaptive User Interfaces Examples of AUIs are: Recommendation systems, Syskill & Webert Personalisation systems, Calendar Apprentice Content-based filtering, NewsWeeder Automatically modifying Web page content for display on smartphones

7. Typical Properties:Adaptive User Interfaces AUIs/IUIs concentrate on how the user model is learnt So concentrates on the user interaction, and hence the interface between user and machine

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

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

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

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

12. Typical Properties:Recommender Systems Collaborative System will make recommendation based on what other similar users 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

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

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)

15. Typical Properties:Personalisation Changing view/interface/content to needs and requirements of user Can apply to anything

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

17. Typical Properties:Ambient Intelligence Devices that adapt to changes in their and their user’s environment

18. Problems that AHSes try to solve What problems did Douglas Adams have in ‘HyperLand’? Chris: take a photo of the problems! Do you experience any other problems when you’re: Browsing? Searching?

19. Typical Problems… Lost in Hyperspace (Otter2000.pdf) Cognitive overload Complexity of the search space Search-browsing Static hypertext structure Inability to cater for different users with different needs and requirements

20. Lost in Hyperspace syndrome Characteristics User doesn't know where he/she is in relation to other (related) information in hyperspace User doesn't know how to access previously visited node Reference Nielsen, J., Lyngbæk, U., Two field studies of hypermedia usability, in Green, C., McAleese, R., (ed) Hypertext: Theory into Practice II, Intellect Press, 1990.

21. Lost in Hyperspace syndrome Causes Bad GUI design Poor organisation of information No links to landmark sites No/poor organisation of access history User unfamiliarity with content/ organisation

22. Cognitive Overload Characteristics Affects both users and authors Follow links “just in case” Too many links on a page to make decision Don’t know when to abandon search References Conklin, J. (1987). Hypertext : An Introduction and Survey. IEEE Computer, 20 (9), 17-41. Nielsen, J. (1990). Navigation through hypertext. Communications of the ACM, 22, 297-310. 18

23. Cognitive Overload Causes Massive information spaces Authors expected to provide links to all relevant information... ... users won't have to think about the information they want to consume... ... implies that authors have to anticipate all the different ways in which their information might be relevant to different users

24. Cognitive Overload Causes (continued) Authors cannot anticipate all the ways in which their information might be used Authors cannot know all relevant information that exists/might exist in the future Worst-case scenario: link everything to everything else... ... but users will then face cognitive overload Every time user accesses a node with more than one out-link, she has to decide which link to follow

25. Cognitive Overload Causes (continued) Hyperspaces don't give guarantees about connectedness/ completeness A user's interaction with an information base improves with familiarity The more complex the organisation, the harder for the user to develop an accurate conceptual model of the site Users also face cognitive overload in environments where the interface is inconsistent 7+/-2 (George Miller, Miller’s Magic Number, Short-term memory)

26. Complexity of the search space Characteristics Cannot “guess” how document might be described Cannot “guess” where a document might be located Related to Cognitive Overload References Staff, C. (2001). HyperContext: A Framework for Adaptive and Adaptable Hypertext. PhD thesis, University of Sussex. Chapters 2 and 3 (http://staff.um.edu.mt/csta1/HCT/thesis/) Van Dyke Parunak, H. (1989). Hypermedia topologies and user navigation, in Proceedings of the second annual ACM conference on Hypertext. November 1989.

27. Complexity of the search space Causes Browsing is a form of search "I'll know I've found what I want when I see it" Advantage and disadvantage of hierarchical 'classification' systems/Web directories (e.g., http://www.yahoo.com) User has to know where to find what she wants Document must be classified correctly

28. Complexity of the search space Causes Lack of link semantics - no guarantee that in general hyperspaces a link is going to lead to greater detail The more complex the search space, the harder for the user to construct an accurate conceptual model … and the greater the likelihood of the user getting disoriented If the hyperspace is too simple, it probably won't cater for the needs of all possible users If hyperspace is too small, we can 'remember' where everything is

29. Search-browsing I Searching and browsing are complementary tools for navigating through a hyperspace which does not provide a semantic representation of its contents Normally, search is used to identify a node that is "close" to the required node, if not the required node itself Browsing takes place so the user can understand the information content and make informed decisions about which link to follow (or to describe an information need) Sometimes also necessary, because user is unfamiliar with terminology, so needs to locate nodes which will enable user to specify more accurate query

30. Search-browsing II If user knows where information is, or if user knows how to get to information (from some known landmark), then search not needed If user knows what is wanted, but doesn't know location of document, then search is required Usually, search is performed at a different location from which user is browsing Search-browsing would allow user to search while browsing, and the system may enable the user to follow a recommended path to the relevant node

31. Static Hypertext Structure (WWW) Hypertexts are usually static Authors create hyperspace, which users traverse Hypertext generally cannot re-organise itself by learning from users Users who want to create a more easily navigable hyperspace need to create it, possibly by replicating existing resources Links cannot be modified to lead to more useful information, unless the user is the owner of the node (but see XPointer, XLink W3C standards)

32. User-Adaptive Systems • Generally, user adaptive systems can either attempt to ‘understand’ the domain/interaction using semantic approaches or else use surface-based approaches typically utilising IR services to find relevant material

33. Generic & ITS-based AHSs AHS has its roots mainly in ITS and UM Typically, all UAS have a User Model Predominantly domain specific Information space typically designed and created by collaborating educationalists Consistency Known dependency between educational items

34. Generic & ITS-based AHSs AHS has its roots mainly in ITS and UM User generally interviewed to determine skill level User has known or determinable goals System drives the interaction as tutor System decides what user should see next

35. Generic & ITS-based AHSs Another direction for AHS/UAS is from Information Retrieval Aim is to assist users navigating through arbitrary information spaces Bringing relevant information “closer” Uses links as a mechanism for navigation

36. Generic & ITS-based AHSs Characteristics of IR-based AHS Many non-collaborating authors Heterogeneous information space Inconsistency Links may provide clues about relationship between information items Inexact or unknown user goals User determines what to see next System attempts to guide user to relevant documents

37. Generic & ITS-based AHSs Generic Architecture

38. Generic & ITS-based AHSs ITS-based architecture Adapted from http://coe.sdsu.edu/eet/Articles/tutoringsystem/start.htm

39. 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