Adaptive Presentation for the Web

Adaptive Presentation for the Web

INFSCI 2955 Adaptive Presentation for the Web Session 4-1 Peter Brusilovsky School of Information Sciences University of Pittsburgh, USA http://www.sis.pitt.edu/~peterb/2955-092/ 1 With slides of Worasit Choochaiwattana, INFSCI 3954 The Adaptive Web Adaptive

multimedia presentation Natural Inserting/ language removing adaptation

fragments Adaptive text Altering Adaptive presentation fragments presentation Adaptation of

Canned text Stretchtext modality adaptation Adaptive Sorting hypermedia Direct guidance fragments

technologies Adaptive link Dimming Hiding sorting Adaptive fragments

Adaptive link Disabling navigation support hiding Adaptive link Removal annotation Adaptive link generation

Map adaptation Adaptive presentation: goals Provide the different content for users with different knowledge, goals, background Provide additional material for some categories of users comparisons extra explanations Details Remove irrelevant or already known content

Adaptive Presentation: Focus on the User Comparisons in PEBA-II Comparisons in PEBA-II Layered View to Adaptive Presentation Content adaptation What to present? Select relevant content for presentation

Adaptive presentation How to present? Select presentation approaches for selected content Techniques for Content Adaptation Using canned text Page and Fragment Variants Content generation from various internal representations Approaches Based on Abstract Information Page and Fragment Variants

Adaptable Page Adaptable Page Adaptable Page Adaptable Page Adaptable Page Adaptable Page Adaptable Page

Adaptable Page Model of + user/context Model of +user/context Model of +user/context Model of + user/context Model of + user/context

Model of + user/context Model of + user/context Model of user/context Interaction Context Adaptation Mechanism Presentation

Page Variants Simplest approach for content adaptation Several variants are stored for the same content page Each variant is marked as suitable for specific categories of users One of the variants is selected dynamically to match the given user Example Adaptive help in ORIMUHS Problems Does not scale up to complex adaptation Large number of variants need to be written

Fragment Variants The page presented to the user is constructed by selecting and combining an appropriate set of fragments. Each fragment typically is a self-contained information element, such as a paragraph or a picture Each fragment can be either presented or not presented to a specific user The level of granularity of the adaptation is increased. Optional Fragments

In optional fragments, a page is specified as a set of fragments; each fragment is associated with a set of applicability conditions At runtime, the page is generated by selecting only those fragments whose conditions are satisfied in the current interaction context. Why Optional Fragments? Adding extra features for specific users Additional explanations (MetaDoc) Additional comparisons (PEBA-II) Additional details

Removing fragments, which are irrelevant Do not match the current goal (PUSH) Already well-known (ILEX) Altering Fragments In altering fragments, a page is specified as a set of constituents, and for each constituents there is a corresponding set of fragments. At runtime, the page is created by selecting for each constituent the fragment that is most appropriate in the current interaction context.

Fragment Variants Benefits Once a set of fragments and conditions on their applicability have been written, a large number of pages can be automatically generated to cover a corresponding large number of situations. Problems The selection and assembly of a suitable set of fragments may involve a substantial overhead at runtime. It may be sometimes difficult to combine the set of independently selected fragments into a coherent whole (smoothing approaches using NLG - see Hirst)

Conditional Text Filtering Similar to UNIX cpp Universal technology Altering fragments Extra explanation Extra details Comparisons Low level technology If switch is known and user_motivation is high Fragment 1

Fragment 2 Text programming Fragment K Content Generation It requires an abstract representation of the domain from which the content is selected, as well as of the features of the interaction context to which the content is tailored. Several formalisms have been used to represent the domain and the context (user models) Knowledge Bases : ILEX, HYLITE+ Bayesian Networks : NAG

Preference Models : GEA, PRACMA, SETA Adaptive Presentation from Abstract Information Content Selection/Determination A subset of the domain knowledge is identified. most domain-independent strategies for content selection compute a measure of relevance for each content element and use this measure to select an appropriate subset of the available content Content Structuring Selected fragments are organized in order to be effectively communicated/presented.

This involves not only ordering and grouping them, but also specifying discourse relations between fragments Example: ILEX ILEX Content Selection ILEX Content Selection The content selection strategy is to return the n most relevant knowledge elements.

If the selection process based on relevance cannot fine a sufficient number of knowledge elements, additional content selection routines are activated. The measure of relevance for content selection combines a measure of structural relevance of knowledge element/fact with its intrinsic score. ILEX Content Selection Structural relevance is computed starting form the

focal entity using two heuristics 1. 2. Information becomes less relevant the more distant it is from the focal object, in term of semantic links Different semantic links maintain relevance to different degrees. Intrinsic score of a knowledge element combines numerical estimates of three factors 1. 2.

3. The potential interest of the information to the current user The importance of the information to the systems informational goals The importance of the information given to what extent the user may already know this information ILEX: Interest Adaptation for a user interested in styles for a user interested in designers

This jewel is a necklace and is in the Organic style. It was made in 1976. It is made from opals, diamonds and pearls. Organic style jewels usually draw on natural themes for inspiration (for instance, this jewel uses natural pearls). Organic style jewels are usually

encrusted with jewels. To take an example, this jewel has silver links encrusted asymmetrically with pearls and diamonds. This jewel is a necklace and was made by Gerda Flockinger, who was a designer and was English. The jewel, which is in the Organic style, was made in 1976. Organic style jewels usually draw on natural themes for inspiration; for instance, this jewel uses natural pearls.

Organic style jewels are usually encrusted with jewels; for instance, this jewel has silver links encrusted asymmetrically with pearls and diamonds. Example: RIA RIA (Responsive Information Architect) Multimedia conversation system (real estate recommendation) Multimedia response to a user query (speech or gesture) is tailored to conversation context Automatic response generation optimizationbased Content selection balancing constraints (content

quality & quantity constraints) RIA Multimedia Response RIA: Content selection as an optimization problem. The goal is to identify the most desirable subset of data dimensions in the current interaction context. The desirability of each data dimension is computed as the linear combination of a large set of feature-based metrics that characterize how important the dimension is with respect to the interaction context. Most of these feature are labeled as content relevance features and include features of the data, features of user, as well as features relating the dimension to the user request and the interaction history.

Once data dimensions have been assigned their desirability, RIAs content selection strategy returns the set of data dimensions such that their overall desirability is maximized and their cost is within given space and time allocated for the target presentation. Content Structuring This involves not only ordering and grouping them, but also specifying what discourse relation must hold between the resulting groups Schemas are the method of choice to accomplish all these tasks and are commonly implemented with taskdecomposition planner

Techniques for Content Presentation Priority on Focus Relevance-Based Techniques for Content Presentation Priority on Context Media Adaptation Stretchtext Dimming

Fragment Scaling Fragment Relevance-Based Techniques Two general dimension Maintaining Focus Maintaining Context Context is more easily maintained if much of the original content is visible to the user. The more content is shown, the higher the chances of generating information overload and reducing attention to the most relevant information.

Priority on Focus All of the techniques in this category choose to maximize focus by Showing only the most relevant content Precluding access to the rest of the context. The two main drawbacks: The user has no way to recover from bad adaptation They do not allow for user control Scrutability interface may ease this drawback Scrutable Adaptive Presentation

in SASY Priority on Context Stretchtext Preserve focus by hiding the less relevant content. Dimming Fragments Deemphasize content by fading its color Scaling Fragments (AKA Fisheye) Deemphasize content by reducing size Example: Stretchtext (PUSH)

Example: Scaling Example: Scaling Scaling vs. Stretchtext Tsandilas and Schraefel pointed out that Stretchtext performed better on larger pages. 4 of 6 subjects gave a higher score to scaling because they felt it provides better information on the content of the deemphasized paragraphs. For more details, http://wwwis.win.tue.nl/ah2003/proceedings/ht-5/

Technique for Media Adaptation Adapting the medium (e.g. text, graphic, spoken language) Factors Relevant for Media Adaptation Example of System Media Adaptation Approaches Rule-base approach Optimization approach Factors Relevant for Media Adaptation Factors Relevant for

Media Adaptation User-Specific Features Preferences Abilities Accessibility Information Features Media Constraints Limitations of

Technical Resources Example of System The CUMAPH adaptive hypermedia environment adapts hypermedia documents according to user profile that describes the users cognitive abilities. The AVANTI system adapts the media according accessibility issues and resources issues. For more details, http://www.contrib.andrew.cmu.edu/~plb/UM97_workshop/Fink/Fink. html Rule-Based Approach

The vast majority of systems that perform media adaptation are using rules that describe how to best convey the target information given subsets of the factors. Arens et al. describe a system that can adapt the media based on characteristics of the information to be conveyed, media constraints, the users interests and abilities, and the overall goals of the information presentation. Rule-Based Approach Result Check

Apply Presentation Structure Discourse Structure Media allocation rules Layout Specialist Optimization Approach Formulate the media adaptation process as an optimization problem. CUMAPH (Cognitive User Modeling for Adaptive

Presentation of Hyper-Document) use two metrics : one for the media combination that best fits to the user profile; the other for combining multiple media. The system generates all possible combinations of media assignments to information item and picks the one whose sum of the two metrics is the highest. Optimization Approach The advantage of the optimization approach are Not require a large set of rules. Allow system to handle issues with conflicting or interdependent factors without a large amount of communication among different

system components. More easily extended More easily to transferred to different domains References Adaptive Presentation for the Web by Andrea Bunt, Giuseppe Carenini and Cristina Conati Adaptive Presentation Supporting Focus and Context by Theophanis Tsandilas and m.c. Schraefel Personalised hypermedia presentation techniques for improving online customer relationships by Alfred Kobsa, Jurgen Koenemann and Wolfgang Pohl.

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