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ΕΠΛ 435: Αλληλεπίδραση Ανθρώπου Υπολογιστή

ΕΠΛ 435: Αλληλεπίδραση Ανθρώπου Υπολογιστή. User Modeling, Adaptation, Personalization Part 2. In this lecture. User profiles Main techniques for acquiring user profiles Examples for representing user profiles. USER PROFILE. USER MODEL APPLICATION. USER MODEL ACQUISITION.

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ΕΠΛ 435: Αλληλεπίδραση Ανθρώπου Υπολογιστή

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  1. ΕΠΛ 435:Αλληλεπίδραση Ανθρώπου Υπολογιστή User Modeling, Adaptation, Personalization Part 2

  2. In this lecture User profiles • Main techniques for acquiring user profiles • Examples for representing user profiles Τμήμα Πληροφορικής

  3. USER PROFILE USER MODELAPPLICATION USER MODELACQUISITION INFORMATION ABOUT U ADAPTING TO U General Architecture of User-Adaptive Systems Τμήμα Πληροφορικής

  4. User Profiles Include general information about the users • Demographic information • Name • Age • Country • Education level • … • User interests • List of key words • List of topics • … • User preferences • Disabilities • Preferred interaction style • Preferred media • … Τμήμα Πληροφορικής

  5. USER PROFILE USER MODELAPPLICATION USER MODELACQUISITION INFORMATION ABOUT U ADAPTING TO U General Architecture of User-Adaptive Systems Identify the user Collect information about the user Τμήμα Πληροφορικής

  6. Methods for User Identification • Cookies • Easiest and most widely deployed • Accuracy can be poor • Data aggregators • Example: Acxiom (www.acxiom.com) • Provide demographic information about customers • Software agents • Small programs that reside on the user’s computer • Collect information about the user and share with a server via some protocol • More control over the implementation Τμήμα Πληροφορικής

  7. Methods for User Identification • Logins • Users identify themselves upon login • Can access information from different computers • Accuracy pretty good • Enhanced proxy servers • User registers their computer with a proxy server • User identification via identification of the computer • Usage of several computers (all should be registered to the proxy server) • Session IDs • Store information about the user per visit • Short-term user profile, not suitable for long term Τμήμα Πληροφορικής

  8. Methods for User Information Collection • Explicit information collection • Information entered by the user • Most common – use forms • Fairly reliable • Complies with privacy regulations • Requires time and willingness to contribute • Can be obtrusive • Dynamic changes can be missed Τμήμα Πληροφορικής

  9. Methods for User Information Collection • Implicit User Information Collection (Gauch et al., 2007) Τμήμα Πληροφορικής

  10. Explicit vs Implicit Information Collection • Studies are inconclusive • Earlier research – support for explicit • Most recent research – support for implicit • Agreement – combined performs reasonably well Τμήμα Πληροφορικής

  11. User Profile: example • 3D shopping mall: www.activeworlds.com • Demographic data • User preferences • Usage data sensing • Seen products • Clicked products • Cart products Interest ranking Luca Chittaro and Roberto Ranon, Adaptive 3D Web Sites, P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 433 – 462, 2007. Τμήμα Πληροφορικής

  12. User profile: data base table Τμήμα Πληροφορικής

  13. User profile: xml Τμήμα Πληροφορικής

  14. USER PROFILE USER MODELAPPLICATION USER MODELACQUISITION INFORMATION ABOUT U ADAPTING TO U General Architecture of User-Adaptive Systems CBF Τμήμα Πληροφορικής

  15. Why do we need to build cognitive models of users • Examples: • User-adaptive mortgage consultant • User-adaptive computer store • Ticket assistant (e.g. COLLAGEN) • Recommender systems (hybrid models) • Employing cognitive models of users to improve adaptation • Decide when explanations/help are needed • Tailor help to the user’s level of understanding • Decide what content to include and how to structure it • Filter recommendations and tune recommender algorithms Τμήμα Πληροφορικής

  16. User model Expert knowledge Expert-based user modelling Maintaining the user model System-user interface Τμήμα Πληροφορικής

  17. Representation of knowledge (1) • Frames Frame: country capital (town) location language currency climate popular resorts (resort) Frame: town population nearest airport location Frame: resort nearest airport activities cultural events Τμήμα Πληροφορικής

  18. Representation of knowledge (2) • Frames Country: Spain capital: Madrid location: Europe language: Spanish currency: Euro climate: warm continental popular resorts: Barcelona, Madrid, Malaga, Toledo Town: Madrid population: 3 mln nearest airport: Madrid location: central Spain Resort: Malaga nearest airport: Malaga activities: swimming, golf, beaches cultural events: Cathedral, Museum of Picasso Τμήμα Πληροφορικής

  19. Representation of knowledge (3) • Semantic networks • Powerful representation but difficult to infer Continent Location Spain part-of has isa Country Language has has has Capital Town isa Τμήμα Πληροφορικής

  20. Representation of knowledge (4) • Logical systems – most often belief modal logic Belief set B(U,p1) B(U,¬p2) B(U,p3) B(U,p3=>p4) B(U,p5=>p1) B(U,p6=>¬p1) Example Reasoners applied by the user R1: ()   Modus Ponens R2: ()   Modus Tolens R3: ()   R4: ()   Apply the reasoners to derive additional beliefs of the user, called DERIVED BELIEFS. Τμήμα Πληροφορικής

  21. Beliefs, knowledge, misconceptions • Beliefs inferred based on user behaviour • Correct beliefs considered knowledge • Incomplete and erroneous beliefs considered misconceptions User belief set B(u,p1) B(u,¬p2) B(u,p3) B(u,p3=>p4) B(u,p5=>p1) B(u,p6=>¬p1) System belief set B(s,p1) B(s,p2) B(s,p2=>p4) B(s,p5=>p1) Identify knowledge and misconceptions Τμήμα Πληροφορικής

  22. USER MODEL USER MODELAPPLICATION USER MODELACQUISITION INFORMATION ABOUT U ADAPTING TO U Schema of User-Adaptive Systems Τμήμα Πληροφορικής

  23. Two steps • Content adaptation – what content is most appropriate for the current user based on the user model • Content presentation – how to most effectively present the selected content to the user Τμήμα Πληροφορικής

  24. Show to user Select pages Page-based approaches • Pre-defined pages • The adaptation mechanism selects the most appropriate page UM Advantages and disadvantages? Τμήμα Πληροφορικής

  25. Example: KBS Hyperbook • Adaptive Information Resources • Adaptive Navigational Structure • Adaptive Trail Generation • Adaptive Project Selection • Adaptive Goal Selection Τμήμα Πληροφορικής http://wwwis.win.tue.nl/asum99/henze/henze.html

  26. Example: AHA • Navigation frame (generated by the system) • Content frame – combines fragments prepared by authors • Inclusion/exclusion of links; • Inclusion/exclusion of detail Τμήμα Πληροφορικής http://aha.win.tue.nl/

  27. Dynamic approaches • Content adaptation: • Dynamic selection of content • Dynamic structuring of the content • Content presentation • Defining relevance and focus • Dynamic media adaptation Τμήμα Πληροφορικής

  28. Dynamic content adaptation • Content automatically selected from: • Knowledge base, relevance measures (e.g. ILEX, STOP) • Bayesian networks expressing causal probabilistic relationships between variables from the domain (e.g. NAG) • User preferences model, importance measures(e.g. GEA, RIA) • Content automatically structured: • Task- accomplished planners • Argumentation models • Conversation theories Τμήμα Πληροφορικής

  29. Example: ILEX http://www.hcrc.ed.ac.uk/ilex/ • Domain Model • The Content Potential • Text Structure • Syntactic Structure • Presentational Forms • Representation of Context Τμήμα Πληροφορικής

  30. Example: GEA (Carenini & Moore, 2001) • User preferences in a hierarchical model (e.g. house, location, number of bedrooms) • Argument structure tailored to user preferences (uses measure of relevance) • Level of detail will differ for users or for the same user at different stages Τμήμα Πληροφορικής

  31. Example: RIA http://www.research.ibm.com/RIA/ Two different responses to the same query depending on user preferences Τμήμα Πληροφορικής

  32. Dynamic content presentation Maintaining focus and context • Focus – emphasise the content that has been found most relevant to the user • Context – allow access to less relevant content to preserve context • Stretch text • Scaling fragments • Dimming fragments • Summary thumbnail Τμήμα Πληροφορικής

  33. Example: scaling approach Follows the “fish eye” visualisation Technique Adaptation of an online guide about cultural events in Toronto: http://whatsuptoronto.com/ Τμήμα Πληροφορικής

  34. Dynamic content presentation Media adaptation: factors • User-specific features • Information-specific features • Contextual information • Media constraints • Limitations of technical resources Τμήμα Πληροφορικής

  35. Dynamic content presentation Media adaptation: approaches • Rule-based approaches • Using rules to define how to take into account the media factors in media selection • Optimisation approaches • Given the media factors, find the media combination that produces the most optimal result Τμήμα Πληροφορικής

  36. Example: RIA Optimisation adaptation http://www.research.ibm.com/RIA/ The optimization procedure deals with: (1) suitability of the information to the media; (2) increase recallability; (3) maintain presentation consistency Τμήμα Πληροφορικής

  37. Καλό Βράδυ Τμήμα Πληροφορικής

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