1 / 45

Personalized Web Interaction

Intelligent Information Processing (IIP-2002) Montreal, 29 August 2002. Personalized Web Interaction. Wolfgang Wahlster. German Research Center for Artificial Intelligence DFKI GmbH Stuhlsatzenhausweg 3 66123 Saarbruecken, Germany phone: (+49 681) 302-5252/4162 fax: (+49 681) 302-5341

tahlia
Download Presentation

Personalized Web Interaction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Intelligent Information Processing (IIP-2002) Montreal, 29 August 2002 Personalized Web Interaction Wolfgang Wahlster German Research Center for Artificial Intelligence DFKI GmbH Stuhlsatzenhausweg 3 66123 Saarbruecken, Germany phone: (+49 681) 302-5252/4162 fax: (+49 681) 302-5341 e-mail: wahlster@dfki.de WWW:http://www.dfki.de/~wahlster

  2. Three Generations of Webpages First Generation Second Generation Third Generation Virtual Webpages Interactive Web- pages Static Webpages Netbots, Information Extraction, Presentation Planning JavaScripts and Applets User Modeling, Machine Learning, Online Layout Database Access and Template-based Generation Fossils cast in HTML Dynamic Web- pages Personalized Webpages

  3. Outline of the Talk • 3rd Generation Websites: Adaptive and Virtual Webpages • The Need for Personalization • Component Technologies for Personalized Web Interaction • - Constraint-based Layout • - Plan-based Presentation Generation • 4. Multimodal Interaction with Personalized Presentation Agents • 5. Affective Personalization • 6. Conclusions

  4. What is a Virtual Webpage? Virtual Memory, Virtual Relation, Virtual Reality... A Virtual Webpage l is generated on the fly as a combination of various media objects from multiple websites or as a transformation of a real webpage. l looks like a real webpage, but is not persistently stored. l integrates generated and retrieved material in a coordinated way. l can be tailored to a particular user profile and adapted to a particular interaction context. l has an underlying representation of the presentation context so that an Interface Agent can comment, point to and explain its components.

  5. Virtual Webpage Retrieved from 5 Different Servers

  6. The Basic Structure of an Adaptive Software System Data about user Collects User Modeling Constructs System User Model Produces Adaptation Adaptation Effect adapted from: P. Brusilovsky, M. Maybury: From Adaptive Hypermedia to the Adaptive Web, CACM, May 2002, Vol. 45, Nr. 5

  7. Two Meanings of Personalization Personalization = Adaptation of a Webpage to a Particular User Personalization = Presentation of a Webpage by a Life-Like Character

  8. Personalized Web Interaction: A Transdisciplinary Field Human Computer Interaction HCI Information Retrieval IR Personalized Web Interaction Artificial Intelligence AI

  9. Major Application Areas for Personalized Web Interaction l Adaptive Navigation and Search l Adaptive Content Selection l Adaptive Presentation Recommender Systems Info- tainment Mobile Services E-Business E-Learning

  10. The Need for Personalization: Adaptive Interaction with Mobile Devices ? e.g. 60 x 90 pixel b/w e.g. 1024 * 768 pixel 24-bit color

  11. From Explicit to Implicit Input for User Model Acquisition Explicit Implicit Self-Reports on Personal Characteristics Previously Stored Information Self-Assessments on General Dimensions Naturally Occuring Actions User Model Acquisition Evidence for Affective And Cognitive States Evaluation of Specific Items Evidence about Context Response to Test Items cf.: A. Jameson, Designing Systems that Adapt to their Users, CHI 2002

  12. A “Web of Meaning“ has more Personalization Potential than a “Web of Links“ OWL DAML + OIL high Content XML medium Structure HTML low Layout Three Layers of Webpage Annotations Personalization Potential cf.: Dieter Fensel, James Hendler, Henry Liebermann, Wolfgang Wahlster (eds.) Spinning the Semantic Web, MIT Press, November 2002

  13. Mapping Web Content Onto a Variety of Structures and Layouts Personalization OWL Content XML2 XMLn XML1 Structure Layout HTML1m HTML21 HTML2o HTML31 HTML3p HTML11 From the “one-size fits-all“ approach of static webpages to the “perfect personal fit“ approach of adaptive webpages

  14. The Architecture of WIP (cf. [Wahlster et al. 91]): The First System for the Plan-based Personalization of Presentations Presentation Planner Layout Manager Presentation Goal Text Design Graphics Design Generation Parameters Illustrated Document Document Design Plan Text Realization Graphics Realization Application Knowledge coded in RAT ... Mower Graphics Design Strategies Basic Ontology User Model Presentation Strategies TAG Espresso Machine Modem RAT

  15. Component Technologies for Personalized Web Interaction User Modeling Machine Learning Intelligent Tutoring Adaptive Layout Presentation Planning Language Technology Agent Technology Ontologies Hypermedia Personalized Web Interaction

  16. Creating Personalized Webpages (cf. EU project IMAGEN: www.imagenweb.org) Webpages Webpages Content Authors Portal Provider MyArt www Portal Content Customers Tailoring to the constraints of various display devices

  17. Creating a New Webpage from Available Media Objects 1. Content Selection 2. Content Packaging 3. Layout Revision

  18. Finding Adequate Layouts Options byConstraint Solving Possible Solutions Finite domain constraint solver Package ensure-contrast title-size-min title-size-hierarchy regular-less-title regular-size-equals CSP

  19. Example of a Layout Constraint (Kröner 2002) Purpose use a title font that is larger than all fonts of subtitles Approach Add a binary constraint between the related font sizes Constraint Formulation <lm:constraint name=“title-size-hierarchy” src=“JCL.BC_ID_LessThan” target-var=“lm:font-size” target-node=“//ims:title” source-node=“../ancestor::*/child::ims:title” />

  20. Exploiting User Preferences to Select a Solution Selecting a Solution Delivery Possible Solutions

  21. AiA: Information Integration for Virtual Webpages PAN Travel Agent Andi Car Route Planner Yahoo News Server Yahoo Weather Server Gault Millau Restaurant Guide Hotel Guide

  22. The Generation of Virtual Webpages with PAN and AiA Address Hotel Agent Map Agent AiA Presentation Planner Pictures and Graphics Netbot PAN Pieces of Text Components of virtual Webpages Virtual Web Presentation Coordinates for Pointing Gestures Trip Data Input for Speech Synthesis Persona Server Icons for Hyperlinks Constraint- based Online Layout Weather Agent Train & Flight Scheduling Agent Major Event Agent

  23. Persona as a Personal Travel Consultant

  24. The Combination of Retrieved and Generated Media Objects for Virtual Webpages Multi-Domain Problem Specs NETBOT Multiple Data Sources Distributed Information Information Structures l Relations, Lists l KR Terms Media Objects l Texts, Sounds, Videos l Pictures, Maps, Animations Retrieved Results

  25. The Combination of Retrieved and Generated Media Objects for Virtual Webpages Information Structures l Relations, Lists l KR Terms Media Objects l Texts, Sounds, Videos l Pictures, Maps, Animations Retrieved Results Select Canned Media Objects Design New Media Objects Coordinate Media Objects Transform Media Objects l Icons, Clip Art l Frames, Sounds l Graphics, Animation l Text, Speech, Facial Expr. l Temporal Synchroni- zation l Spatial Layout l Clip, Convert, Abstract l Zoom, Pan, Transition Effects Select & Design Reuse & Transform

  26. Our Approach to the Autoanimation of Life-LikeCharacters Events & UserIntervention Tasks self-behavior Presentation Planner Persona Player Script

  27. The planning process decomposes complex presentation tasks until elementary presentation acts are worked out Describe-item Describe-feature S-Include-image S-Speak S-Point It has broad tires Introduce Design-Intro-Page Emphasize S-Include-Hyperlink S-Include-header S-Speak illustrate Describe item Welcome to the Porsche website S-Include-images 911 & Boxter The New Models

  28. From the Presentation Task to the Executable Script determine acts determine script play presentation e.g., describe a location presentation task

  29. DFKI’s Presentation Planner Can be Used to Generate Scripts for Various Player Technologies Presentation Planner PET- PML Agent Script SMIL APML Java Persona Player PET MPEG4 Greta MS-Agent Controller SMIL Player (Real, QuickTime)

  30. Plan-based Generation of Input to a MPEG4 Player(e.g., Greta-Player by C. Pelachaud) <?xml version="1.0"?> <APML> <meta-cognitive type="i'm thinking">I have been in <affective type="distress"> jail </affective>for a very <topic-comment type="comment"><adjectival type="large"> long </adjectival> </topic-comment>period.</meta-cognitive> </APML> FAP(Facial Animation Parameter) 2.1 a1_1_2 25 163 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 0 0 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 9 9 -1 -1 9 10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Affective Presentation Markup Language (de Carolis et al.)

  31. Personalization Through Multimodal Dialogue Agents: The User Converses with a Presentation Agent • August (KTH) • Cyberella (DFKI) • Peedy (Microsoft) • REA (MIT) • Smartakus (DFKI) • …

  32. SmartKom`s SDDP Interaction Metaphor Webservices Service 1 Personalized Interaction Agent User specifies goal delegates task Service 2 cooperate on problems asks questions presents results Service 3 SDDP = Situated Delegation-oriented Dialogue Paradigm See: Wahlster et al. 2001 , Eurospeech

  33. SmartKom: A Transportable Interface Agent Media Analysis Kernel of SmartKom Interface Agent Application Manage- ment Media Design Interaction Management SmartKom-Mobile: A Handheld Communication Assistant SmartKom-Public: A Multimodal Communication Kiosk SmartKom-Home/Office: Multimodal Portal to Information Services

  34. Personalized Interaction with WebTVs via SmartKom (DFKI with Sony, Philips, Siemens) Example: Multimodal Access to Electronic Program Guides for TV User: Switch on the TV. Smartakus: Okay, the TV is on. User: Which channels are presenting the latest news right now? Smartakus: CNN and NTV are presenting news. User: Please record this news channel on a videotape. Smartakus: Okay, the VCR is now recording the selected program.

  35. - different role castings, e.g. to emphasize: - different point of views / level of expertise, teacher/student, seller/buyer, - the indexing of contributions, e.g. according to: - Type of info: “A reports on X, B on Y” - Source of info: “A says X, B says Y” Require: - appropriate distribution of contents to the different characters - believable dialogues Presentation Teams Presentation Teams

  36. Presentation Teams in the MIAU Project(Wahlster, Rist, André, Baldes 2001)

  37. The Adaptation of Presentation Agents in MIAU Character-Centered Approach Story is not defined by a script, but by the character‘s role, personality, status, attitude etc.

  38. Interactive Presentation Teams in MIAU (DFKI) Hello, I am Peter.

  39. The Development of Personalized Webpages multiparty, multithreated conversations interactive performances, iTV Non-interactive presentation Hyper- presentation / dialogue Presentation teams 2002 PPP AIA MIAU

  40. Personalized Multiparty Interaction with Multiple Interface Agents

  41. Tailoring Web Presentations to the User's Affective State Inside the instrument there is a thermometer that measures the hand's temperature. In the place where the fingertips touch there is an area sensitive to sweat. Heartbeats are measured by the pulse of the thumb. Movements are analyzed by a sensor next to the mouse's ball. IBM's prototype Emotion Mouse measures heart rate, temperature, general somatic activity, and the galvanic skin response.

  42. Using Facial Expression Recognition forAffective Personalization Processing ironic or sarcastic comments (1) Smartakus: Here you see the CNN program for tonight. (2) User: That’s great.  • Smartakus: I’ll show you the program of another channel for tonight. • (2’) User: That’s great.  (3’) Smartakus: Which of these features do you want to see?

  43. Personalized Car Entertainment (DFKI for Bosch) MP3 music files from the Web Rist & Herzog for Blaupunkt

  44. Recent Overviews about Personalized Web Interaction l Peter Brusilovsky, Oliviero Stock, Carlo Strapparava (eds.) Adaptive Hypermedia and Adaptive Web-based Systems, First International Conference, AH 2000, Trento, August 2000, LNCS 1892 l Peter Brusilovsky, Mark Maybury (eds) The Adaptive Web. CACM, Vol. 45, Issue 5, May 2002 l Peter Brusilovsky, Carlo Tasso (eds.): Special Issue on User Modeling for Web and Hypermedia Information Retrieval, In: User Modeling and User- Adapted Interaction: The Journal of Personalization Research, Kluwer, 2002 l Jameson, Anthony: Designing Systems that Adapt to Their Users. Tutorial presented at CHI 2002, Minneapolis, May 2002, ACM, Tutorial Notes 13 l Paul de Bra, Peter Brusilovsky, Ricardo Conejo (eds): Adaptive Hypermedia and Adaptive Web-based Systems, Second International Conference, AH 2002, Malaga, May 2002, LNCS 2347

  45. Conclusions • The goal of adaptive personalization is to increase the • usage and acceptance of ubiquitous webservices. • The intelligent adaptation to cognitive and technical • resource limitations of the user is an important • prerequisite of user-friendly web interaction. • Plan-based and constraint-based methods are now • available for the advanced personalization of adaptive • and virtual webpages. • The Semantic Web is increasing the potential for • the effective personalization of webpages.

More Related