Post on 27-Jan-2015
description
“interface is the message”
on the path to a usable & personal Semantic Web
Lora AroyoVU University Amsterdam
@laroyo
1Wednesday, June 1, 2011
front-end to semantics: how do we interact with SemWeb Apps?
personalization: what do we need to adapt to users?
example applications: what good & bad is out there?
evaluation: why is continuous evaluation so important?
outline
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invisible computers
multitude of interaction modes
context-sensitive apps
networked devices: bridges between virtual & physical worlds
GUI become central
constantly increasing competition
why interfaces?
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combine content semantics with user context
integrate seamlessly physical & web worlds
identify relevance to user to rank & select information to present
continuous feedback cycle: to and from user
you need to deal with GUI on configuration level
perform continuous user testing
use real world data
take home message
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“interface is the message”
Aaron Koblin: Artfully visualizing our humanity, TED Talk, 2011
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FRONT-END TO SEMANTICShow do we interact with the SemWeb Apps?
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do SemWeb apps really differ?
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explicit semantics (often from open sources, e.g. LOD) used for system decisions and results
use facetted presentation, searching and browsing of information
use typically classifications, typologies or other structures of concepts
integrate data from different sources
aggregate data
semantics: what’s special?
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credits: Dan Brickley
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RDF data
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interaction with semantics
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© BBC MMVIIIhttp://twitpic.com/il1w/full
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http://www.bbc.co.uk/programmes/b00c06n2.rdf
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converting vocabularies
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PERSONALIZATIONwhat do we need to adapt to us?
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when we consider interaction & interfaces, then the user plays a key role
for good interface design, a good characterization of the user is needed
first, some concept from theory and literature
the user matters
16Wednesday, June 1, 2011
Definition: A ‘user profile’ is a data structure that represents a characterization of a user (u) at a particular moment of time (t)
So, a user profile represents what (from a given (system) perspective) there is to know about a user.The data in a user profile can be explicitly given by the user or have been derived.
user profile
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Personal dataFriend and relationsExperienceSystem accessBrowsing historyKnowledge (learning)Device dataLocation dataPreferences
user characteristics
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Definition: The ‘user model’ contains the definitions and rules for the interpretation of observations about the user and about the translation of that interpretation into the characteristics in a user profile.
So, a user model is the recipe for obtaining and interpreting user profiles.
user model
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Definition: ‘user modeling’ is the process of creating user profiles following the definitions and rules of the user model. This includes the derivation of new user profile characteristics from observations about the user and the old user profile based on the user model.
So, user modeling is the process of representing the user.
user modeling
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Stereotyping is one example of user modeling.
A user is considered to be part of a group of similar people, the stereotype.
Question: What could be stereotypes for conference participants (when we design the conference website)?
stereotyping
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Definition: A ‘user-adaptive system’ is a system that adapts itself to a specific user.
Often, a user-adaptive system (or adaptive system, in short) uses user profiles to base its adaptation on.So, designing an adaptive system implies designing the user modeling.
user-adaptive system
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User-adaptation is often used for personalization, i.e. making a system appear to function in a personalized way.
Question: What user profile characteristics would be useful in personalizing the conference’s registration site?Question: How would you obtain those characteristics?
user adaptation
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Device-dependenceAccessibility (disabilities)Location-dependenceAdaptive workflow
Question: Can you give concrete examples for interface adaptation, both the adaptation effect as the prior user modeling necessary?
examples: user adaptation
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Well-studied example of adaptation is ‘adaptive hypermedia’: a hypertext’s content and navigation are then adapted to the user’s browsing of the hypertext.
adaptive hypermedia
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DESIGNING INTERFACES
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Be cooperativeBe informativeBe truthfulBe relevantBe perspicuous (be clear)
dialog principles [Grice]
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Strive for consistencyEnable frequent users to use shortcutsOffer informative feedbackDesign dialog to yield closureOffer simple error handlingPermit easy reversal of actionsSupport internal locus of controlReduce short-term memory load
UI principles [Shneidermann]
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Visibility of system statusMatch between system and real worldUser control and freedomConsistency and standardsError preventionRecognition rather than recallFlexibility and efficiency of useAesthetic and minimalist designHelp users recognize, diagnose and recover from errorsHelp and documentation
usability heuristics [Nielsen]
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modeling the user: what are user’s preferences, interests, history, activities, etc.
modeling the user’s context: e.g. location, time, device
which of all the data available is relevant for this user in this context
also called context-aware
all about the user’s perspective
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switching between one context and another
doing things not only for him/herself, e.g. buying present for a girlfriend
user’s context distributed
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PERSONALIZED INTERACTIONs
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search, e.g. keyword, faceted
browse, story lines, narratives through collections
annotations of multimedia, e.g. (collaborative) tagging, professional annotation of text, images and video, tagging games
explanations, hints, user feedback, e.g. explanation of recommendation results, explanation of autocompletion suggestions
interaction modes
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recommendation systems, e.g. movies, music, art
user statistics and analysis, e.g. user usage data, profile, group profiles, etc.
social networking
typical examples
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Definition: A ‘recommender system’ is a system that recommends to a user, based on her individual interests, items that the user could find interesting.
Examples: music, movies, people, restaurantsTypes: collaborative (reason about similar users), content-based (reason about similar items)Problems: new users, new items, sparsity, gray sheep
recommender systems
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movies & TV programs, e.g. Netflix, MovieLens, TiVo, personalized TV guides
music, e.g. LastFM, Pandora, iTunes Genius
food & tourism, e.g. guides adapted to location, current time, preferences
news, e.g. Google reader, news filters
e-shopping, e.g. Amazon’s recommendations
advertisement, e.g. Facebook personalized ads
art, museums, e.g. personalized search, personalized museum guides
recommender systems
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Collection of activities/context/attention data
Derive interests from this data
Recommender-specific problems, e.g. cold start, over-specialization
Surface items of interest in the ‘long tail’
Cross-domain recommendations
Multi-person recommending
Granular control for users
considerations
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overview of user preferences, e.g. settings, privacy
overview of user interests, e.g. ranking of interests, links to content
overview of user/group activities, e.g. per topics, per activity, per date, over a period, overall
comparative views between users, e.g. LastFM, livingSocial movies user similarity, Twitter similar users to you
different views/visualization over the same set of user data
user profiles & stats
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professional networks & events, e.g. LinkedIn, Mendeley
people, organizations, e.g. Facebook, MySpace
social bookmarking, e.g. Delicious, StumbleUpon, Diggit
GetGlue
Books, e.g. LibabryThing
social networking
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EXAMPLE APPLICATIONSInterfaces & Personalization on SemWeb
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the big guys
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The Recommendation and Like plugins let users share any content they like back to their profile.
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The Activity Feed plugin shows users what their friends are doing on your site through likes and comments.
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activity streams
http://xmlns.notu.be/aair/
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weighted interest
http://xmlns.notu.be/wi
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EXAMPLE 1what do Gerrit Dou and Rembrandt have in common?
http://www.chip-project.org
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enriched Rijksmuseum collection
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style: Baroque
teacher of: Gerrit Dou
teacher of: Nicolaes Maes
teacher of: Ferdinand Bol
self-‐portrait
mili<a
place: Amsterdam,
1625 to 1650
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goal & central role of UM
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Personalized Web Access Online Tour Wizard
personalized experiencePersonalized Mobile Tour
Interactive user modeling
Recommendations of artworks & art topics
Semantic Search
Museum tour maps
Historic timeline
Interactive tours
On-the-fly adaptation
Synchronized user profile
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semantic recommendations
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semantic recommendations
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semantic recommendations
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semantic recommendations
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semantic recommendations
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personalized tours
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personalized tours
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Interactive Museum Guide
h"p://chip-‐project.org 63Wednesday, June 1, 2011
Interactive Museum Guide
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event-based browsing
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dynamic adaptationFor each artwork in the museum:
Related works
Include in the tour ( & recalculate the map/tour)
Indicate relevance in terms of e.g. personal interest, position, recommended by friends, by Rijks, on view
Rate to indicate interest
At any point of the tour:
Include/exclude artworks
Adjust tour length
Change navigation in and outside of the tour
Save for other tours
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EXAMPLE 2professionals vs. lay users on Web 2.0
semantic annotation of Rijksmuseum printshttp://e-culture.multimedian.nl/pk/annotate?
semantic tagging: http://waisda.nl
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Autocompletion with multiple vocabularies
http://slashfacet.semanticweb.org/autocomplete/demos/
http://slashfacet.semanticweb.org/wordnet/search
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EXAMPLE 3semantic television
http://notube.tv
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watching TV in a group
for more details check out our blog at http://notube.tv
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watching TV in a group
for more details check out our blog at http://notube.tv
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watching TV in a group
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watching TV in a groupEnvironment
Interact with the second screen as a group Friend interaction at homeWatching as a group
SynchronizationTV & Second Screenbetween second screens between second screens & TV show content provider
Age15 - 35 years old
Type of Activitiesquiz and betting gameschange camera viewinformation regarding the content of the program textual captions
Type of ProgramSports
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observations
for more details check out our blog at http://notube.tv
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observations
for more details check out our blog at http://notube.tv
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second screen & TV functionalities
shared virtual space voice dubbing subtitles related information quizzes voting & bettingscene-grab & share social interaction live-chat parental advisory uncensored version different camera views
synchronization with second screen“overlay” on top of the main TV-picturecensoringdifferent camera viewsgroup alerts
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CONTINUOUS EVALUATION
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Target users’ characteristics
small groups with 2-4 persons and a male taking the leading role (67%)
middle-aged people in 30-60 years old (75%)
higher-educated (62%)
no prior knowledge about the Rijksmuseum collection (62%)
visit the museum for education (98%)
CHIP users
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Define familiarity with the domain
Define familiarity with collections/vocabularies
Identify use cases
Identify navigation patterns
Identify requirements for user groups
Validate
Contextual observations
User interviews
Model user’s tasks
contextual analysis
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domain exploration
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usability testing
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results
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http://www.cs.vu.nl/intertain/
96Wednesday, June 1, 2011
combine content semantics with user context
integrate seamlessly physical & web worlds
identify relevance to user to rank & select information to present
continuous feedback cycle: to and from user
you need to deal with GUI on configuration level
perform continuous user testing
use real world data
take home message
97Wednesday, June 1, 2011