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CHIP
Semantics-driven Recommendations and
Personalized Museum Tour Generation
Lora Aroyo
Bridging the Gap:
Virtual and Physical Museum Experience
Research Questions
□ Can we use semantic
metadata of cultural
heritage to improve
personalized access
through multiple
devices (e.g. PC,
mobile phone, PDA)?
I: CHIP Approach to Semantics
□ Making museum metadata available in RDF/OWL
□ Making relevant vocabularies available in
RDF/OWL
□ Aligning & enriching vocabularies/metadata
□ Using resulting RDF/OWL graph for building a
combined (virtual and physical) user model
□ Using the above result for (semi)automatic
generation of virtual and physical museum tours
CHIP Demonstrator presentation @ CATCH Symposium
style: Baroque
teacher of: Gerrit Dou
teacher of: Nicolaes Maesteacher of: Ferdinand Bol
self-portrait
militia
place:
Amsterdam
period:
1625 to 1650
RDF/OWL Graph for Building
User Model
□ Users rating:
■ Artworks & artwork properties
■ 20 pre-selected by museum curators
■ rest random
□ Ex.:
■ I like “Jewish Bride”
■ I like creator “Rembrandt”
■ I like style “Baroque”
User Profile
□ Based on Web
standards (FOAF,
RDF)
□ Combining physical
& digital activities
from multiple
sources
□ Reusable
□ Distributed
Artwork Recommender
Serendipity in Recommendations
□ Recommendations based on explicit semantics (for
reusability)
□ Cold-start problem improved
□ User control in what to consider for recommendations
□ Non-trivial related recommendations
□ Faceted browsing
□ Combination of CB and collaborative aspects
Artwork
Properties
Property
Details
(artist)
II: CHIP Approach to the Bridging
Using RDF/OWL User Model for
Generation of Museum Tours
□ Generation of Tours, e.g.
“Masterpieces”, “Favorites”, etc.:
□ positively rated creators
□ related artists, i.e. collaborator & student_of
□ same style & style siblings
□ …
□ topical tours
□ length settings
□ spatial adaptation
Tour Wizard
Mobile Museum Guide
III: Domain & User-centered
Approach
□ Four experiments:
■ I: Recommendations effectiveness: novices vs. experts; AR
helps novices to elicit/clarify preferences from their implicit
knowledge of the collection.
■ II: Rating, sparsity and cold-start; "expert-sorted" sequence of
artworks works very well for first-time users to build user profiles;
"rating artwork and topics" increases the total amount of the user's
contributions and improves precision of recommendations.
■ III: Accuracy and interestingness of recommendations;
semantic relations can enhance the CB recommendations.
■ IV: Ontology-enhanced content-based & collaborative
recommendations; semantic relations do not give a significant
difference for the performance of recommender.
□ Study: interactive museum tours in NL and online guides
□ Study: in-door navigation & identification techniques
□ Planned experiments: tour adaptation with semantic & spatial
restrictions
Research Topics
□ Recommendations with respect to subject/theme
□ Recommendations with respect to semantic relations
□ Recommendations for groups of users
□ Taking into account temporal preferences in
recommendations, e.g. “I am not interested NOW”
□ Adapting the tour sequence on the fly
■ based on position, time, interest, previous activities
□ Adapting the artwork sequence in user profiler
■ improving the cold-start problem
□ Social aspects based on FOAF profile
□ Extension of user profile FOAF specification with
interest property
CHIP Tools & Architecture
□ www.chip-project.org
■ www.chip-project.org/demo
■ http://www.chip-project.org/demo/mobileguide/index.jsp
■ http://www.chip-project.org/spacechip/
■ http://www.chip-project.org/chipICITY
The Team
□ Lora Aroyo
□ Paul De Bra
□ Peter Gorgels
□ Cathy Jager
□ Lloyd Rutledge
□ Mettina Veenstra
□ Natalia Stash
□ Yiwen Wang
Follow-up (1)
□ CATCH2 Agora project
■ social platform with CH objects in explicit
(art)historic context
□ digitally mediated public history
□ historic events model for semantically
relevant relationships between objects
□ community curated historic event
thesaurus linked to museum artifacts
■ Rijksmuseum Amsterdam & Dutch
audiovisual archive Beeld en Geluid
Follow-up (2)
□ CATCHPlus Rijksmuseum deployment
■ Q42, Fabrique
■ CHIP API
■ CATCH Central User Profiling Service
■ Rijksmuseum “Genius” on
Rijksmuseum.nl

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CHIP Demonstrator presentation @ CATCH Symposium

  • 1. CHIP Semantics-driven Recommendations and Personalized Museum Tour Generation Lora Aroyo
  • 2. Bridging the Gap: Virtual and Physical Museum Experience
  • 3. Research Questions □ Can we use semantic metadata of cultural heritage to improve personalized access through multiple devices (e.g. PC, mobile phone, PDA)?
  • 4. I: CHIP Approach to Semantics □ Making museum metadata available in RDF/OWL □ Making relevant vocabularies available in RDF/OWL □ Aligning & enriching vocabularies/metadata □ Using resulting RDF/OWL graph for building a combined (virtual and physical) user model □ Using the above result for (semi)automatic generation of virtual and physical museum tours
  • 6. style: Baroque teacher of: Gerrit Dou teacher of: Nicolaes Maesteacher of: Ferdinand Bol self-portrait militia place: Amsterdam period: 1625 to 1650
  • 7. RDF/OWL Graph for Building User Model □ Users rating: ■ Artworks & artwork properties ■ 20 pre-selected by museum curators ■ rest random □ Ex.: ■ I like “Jewish Bride” ■ I like creator “Rembrandt” ■ I like style “Baroque”
  • 8. User Profile □ Based on Web standards (FOAF, RDF) □ Combining physical & digital activities from multiple sources □ Reusable □ Distributed
  • 10. Serendipity in Recommendations □ Recommendations based on explicit semantics (for reusability) □ Cold-start problem improved □ User control in what to consider for recommendations □ Non-trivial related recommendations □ Faceted browsing □ Combination of CB and collaborative aspects
  • 13. II: CHIP Approach to the Bridging
  • 14. Using RDF/OWL User Model for Generation of Museum Tours □ Generation of Tours, e.g. “Masterpieces”, “Favorites”, etc.: □ positively rated creators □ related artists, i.e. collaborator & student_of □ same style & style siblings □ … □ topical tours □ length settings □ spatial adaptation
  • 17. III: Domain & User-centered Approach □ Four experiments: ■ I: Recommendations effectiveness: novices vs. experts; AR helps novices to elicit/clarify preferences from their implicit knowledge of the collection. ■ II: Rating, sparsity and cold-start; "expert-sorted" sequence of artworks works very well for first-time users to build user profiles; "rating artwork and topics" increases the total amount of the user's contributions and improves precision of recommendations. ■ III: Accuracy and interestingness of recommendations; semantic relations can enhance the CB recommendations. ■ IV: Ontology-enhanced content-based & collaborative recommendations; semantic relations do not give a significant difference for the performance of recommender. □ Study: interactive museum tours in NL and online guides □ Study: in-door navigation & identification techniques □ Planned experiments: tour adaptation with semantic & spatial restrictions
  • 18. Research Topics □ Recommendations with respect to subject/theme □ Recommendations with respect to semantic relations □ Recommendations for groups of users □ Taking into account temporal preferences in recommendations, e.g. “I am not interested NOW” □ Adapting the tour sequence on the fly ■ based on position, time, interest, previous activities □ Adapting the artwork sequence in user profiler ■ improving the cold-start problem □ Social aspects based on FOAF profile □ Extension of user profile FOAF specification with interest property
  • 19. CHIP Tools & Architecture □ www.chip-project.org ■ www.chip-project.org/demo ■ http://www.chip-project.org/demo/mobileguide/index.jsp ■ http://www.chip-project.org/spacechip/ ■ http://www.chip-project.org/chipICITY
  • 20. The Team □ Lora Aroyo □ Paul De Bra □ Peter Gorgels □ Cathy Jager □ Lloyd Rutledge □ Mettina Veenstra □ Natalia Stash □ Yiwen Wang
  • 21. Follow-up (1) □ CATCH2 Agora project ■ social platform with CH objects in explicit (art)historic context □ digitally mediated public history □ historic events model for semantically relevant relationships between objects □ community curated historic event thesaurus linked to museum artifacts ■ Rijksmuseum Amsterdam & Dutch audiovisual archive Beeld en Geluid
  • 22. Follow-up (2) □ CATCHPlus Rijksmuseum deployment ■ Q42, Fabrique ■ CHIP API ■ CATCH Central User Profiling Service ■ Rijksmuseum “Genius” on Rijksmuseum.nl