In this talk, I will review our research attempts to implement different kinds of personalization in the context of relevance-based visualization. The goal of this research stream is to make relevance-based visualization adaptive to user long-term goals, interests, or prospects rather just responsive to short term immediate needs such as query terms. I will present four personalized relevance-based visualization systems: Adaptive VIBE, TalkExplorer, SetFusion, and IntersectionExplorer, For each system, I will present its idea, some evaluation results, and lessons learned. https://doi.org/10.1145/3038462.3038474

1. Personalization in the Context of
Relevance-Based Visualization
Peter Brusilovsky
Jae-Wook Ahn
Denis Parra
Katrien Verbert
University of Pittsburgh
PUC Chile
IBM
University of Leuven

2. Outline
• Problem
• History
– InfoCrystall, VIBE, TileBars
• A quest to Adaptive VIBE
– KS-VIBE, QuizVIBE, Adaptive VIBE
• Combining social and adaptive relevance prospects
in Conference Navigator
– TalkExplorer
– SetFusion
– Intersection Explorer
2

3. Why Relevance Visualization?
• Items might be relevant for a query for
different reasons
– I.e., match different keywords
• Ranked list fuses and hides different
relevance aspects
– Not transparent, not controllable
• Focus on relevant items while keeping
relevance dimensions recognizable? 3

4. 4

5. InfoCrystal (Spoerri 1993)

6. From Venn Diagram to IC
6

7. More "InfoCrystals"
7

8. VIBE (Korfhage, 1991)
8

9. TileBars (Hearst, 1995)
9

10. Towards Adaptive VIBE
• Adaptive nodes
– Social: KS-VIBE
– Knowledge-based: QuizVIBE
• Adaptive topology
– Keyword-based: Adaptive VIBE
– Concept-based: Adaptive VIBE+NE
10

11. KS-VIBE (Ahn et al. 2006)
11

12. 12
Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) A two-level adaptive visualization for information access to open-corpus
educational resources. In: Proc of Workshop on the Social Navigation and Community-Based Adaptation Technologies at the 4th
International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Dublin, Ireland, June 20th, 2006, 497-505.

13. QuizVIBE (2006, Ahn et al.)
Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) QuizVIBE: Accessing Educational Objects with
Adaptive Relevance-Based Visualization. In: Proc. of World Conference on E-Learning, E-Learn 2006,
Honolulu, HI, USA, October 13-17, 2006, AACE, pp. 2707-2714.

14. • User control in personalized Filtering in ROSETTA project
– Users choose to ranks search results according to user profile, query, or both
• α * user profile + (1–α) * user query (α = 0, 0.5, 1)
• Users wanted more control
The motivation for Adaptive VIBE
Personalized
IR system
Personalized
IR system
Ranked list :
User Profile
Ranked list :
User Profile
Ranked List :
User Query
Ranked List :
User Query
Fused
Search
Result
Fused
Search
Result

15. Adaptive VIBE Idea: Query and UM
for Document Space Separation
15https://www.youtube.com/watch?v=Yt1fMEFlLVA&index=2&list=PLyCV9FE42dl7JG_i7m_kvwuYRpfwwJ4iY

16. VIBE based query-profile fusion
User Profile Terms
Query Terms
Documents
Mixing user profile and query terms as VIBE POI

17. VIBE based fusion
Document-query
relevance
Document-query
relevance
Profile-query
relevance
Profile-query
relevance

18. VIBE based fusion (cont’d)
More about
N. Korean
nuclear weapon
More about
N. Korean
nuclear weapon
More about
Generic
Nuclear weapon
More about
Generic
Nuclear weapon

19. VIBE POI presets
“Circular” preset
“Parallel” preset

21. • User profile is added on the same playfield
as user query
• Topology is adaptive
• Mediate between profile (green POI) and
query (red POI) terms
• Browse documents free with control on
profile and query terms
Adaptive topology in VIBE

22. Adaptive VIBE+NE
22

23. Some Study Results
• A sequence of user studies
– Search vs. VIBE vs. VIBE+NE
• Search -> VIBE -> VIBE+NE offers:
– Better visual separation of relevant documents (system)
– Supports better opening relevant documents (user)
• VIBE+NE supports more meanigful interaction
– No degradation found even with active visual UM
manipulation
– While over performance retained or increased
Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA, March
29-April 1, 2015, ACM, pp. 202-212

24. Fusing Multiple Relevance
Prospects in Conference Navigator
• Conference Navigator
• TalkExplorer
• SetFusion
• Intersection Explorer
24

25. Relevance in Conference Navigator
• Classic content-based relevance prospects
– Items that has a specific keyword
• Social relevance prospects
– Items bookmarked by a specific user
• Tag relevance prospects (content+community)
– Items tagged by a specific tag
• Personal relevance prospects
– Several different recommender engines
– Each engine offer one relevance prospect
25
Brusilovsky, P., Oh, J. S., López, C., Parra, D., and Jeng, W. (2017) Linking information and people in a social
system for academic conferences. New Review of Hypermedia and Multimedia.

26. Social Prospect: Details of a talk in CN3
26

27. Social prospect: User schedules
27

28. Tag relevance: Tag page in CN3
28

29. Multiple Recommender Engines
29

30. Challenge
• Idea: Fuse traditional, social, personal relevance
prospects
• Approach: fuse several relevance lists
– Several recommendation approaches
– Items bookmarked by valuable users
– Items tagged by interesting tags
• Challenge: How to make it transparent and keep
users in control
– i.e., allowing to focus on a subset of relevance
prospects
30

31. John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual
interactive recommendation. CHI '08
Related work: PeerChooser
31

32. Related work: TasteWeights
32
Bostandjiev,S.,O'Donovan,J.andHöllerer,T.TasteWeights:avisualinteractivehybrid
recommendersystem.InProceedingsofthesixthACMconferenceonRecommender
systems(RecSys'12).ACM,NewYork,NY,USA(2012),35-42.

33. The Approach
• Using set relevance visualization
– One dimension of relevance = one set
• Agent metaphor to mix user- tag- and
engine-based relevance
– recommender systems are shown as agents
– in parallel to real users collecting talks
– tags are also perceived as agents collecting talks
– users can interrelate entities to find items
33

34. TalkExplorer
• Recommendation engines are shown as agents in parallel to users and tags
• Uses Aduna clustermap library: http://www.aduna-software.com/
34

35. Entity selection
35

36. Canvas area
36

37. TalkExplorer
37
overview selected
talks

38. Interrelations agents and users
38

39. Evaluation
• Setup
– supervised user study
– 21 participants at UMAP 2012 and ACM Hypertext 2012 conferences
• Results
– The more aspects of relevance are fused, the more effective it is for
getting to relevant items. Especially effective are fusions across
relevance dimensions
– The more relevance prospects are merged, the better is the yield, the
easier is to find good items
– Dimensions of relevance are not equal
– ADUNA approach is challenging for beyond fusion of 3 aspects 39
Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of Research Talks
with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article No. 11

40. SetFusion
• Using set relevance visualization in
the familiar Venn diagram form
– One recommendation source = one
set
• Allow controlled ranking
fusion
• Combine ranking with
annotation showing
source(s) of recommendation 40

47. Brief Results of Two Studies
• SetFusion provides strong engaging effect
– Number of engaged users, bookmarked talks,
explored talks doubled
– The effect is larger in UMAP “natural” settings
• SetFusion allows more efficient work
– Increases yield of bookmarks in relation to
overhead actions
• But only 3 dimensions of relevance!
47

48. Intersection Explorer
• Based on ideas of
Talk Explorer
• New approach for
scalable multi-set
visualization
• Try it yourself at
IUI2017 Conference
Navigator
48
Verbert, K., Seipp, K., He, C., Parra, D., Wongchokprasitti, C., and Brusilovsky, P. (2016) Scalable Exploration of
Relevance Prospects to Support Decision Making. In: P. Brusilovsky, et al. (eds.) Proceedings of Joint Workshop on
Interfaces and Human Decision Making for Recommender Systems at 10th ACM Conference on Recommender Systems,
Boston, MA, USA, September 16, 2016, pp. 28-35, also available at http://ceur-ws.org/Vol-1679/paper5.pdf.

49. Intersection Explorer at IUI2017
49
http://halley.exp.sis.pitt.edu/cn3/iestudy3.php?conferenceID=148

50. Readings
• Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) A two-level adaptive visualization for information access to open-
corpus educational resources. Proceedings of Workshop on the Social Navigation and Community-Based Adaptation
Technologies at the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Dublin,
Ireland, June 20th, 2006, pp. 497-505, also available at http://www.sis.pitt.edu/%7epaws/SNC_BAT06/crc/ahn.pdf.
• Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) QuizVIBE: Accessing Educational Objects with Adaptive
Relevance-Based Visualization. In: T. C. Reeves and S. F. Yamashita (eds.) Proceedings of World Conference on E-Learning,
E-Learn 2006, Honolulu, HI, USA, October 13-17, 2006, AACE, pp. 2707-2714.
• Ahn, J. and Brusilovsky, P. (2013) Adaptive visualization for exploratory information retrieval. Information Processing
and Management 49 (5), 1139–1164.
• Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA,
March 29-April 1, 2015, ACM, pp. 202-212
• Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of
Research Talks with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article
No. 11
• Parra, D. and Brusilovsky, P. (2015) User-controllable personalization: A case study with SetFusion. International
Journal of Human-Computer Studies 78, 43–67.
• Verbert, K., Seipp, K., He, C., Parra, D., Wongchokprasitti, C., and Brusilovsky, P. (2016) Scalable Exploration
of Relevance Prospects to Support Decision Making. In: Proceedings of Joint Workshop on Interfaces and Human Decision
Making for Recommender Systems at 10th ACM Conference on Recommender Systems, pp. 28-35, also available at
http://ceur-ws.org/Vol-1679/paper5.pdf.
• Verbert, K., Parra-Santander, D., Brusilovsky, P., Cardoso, B., and Wongchokprasitti, C. (2017) Supporting
Conference Attendees with Visual Decision Making Interfaces. In: Companion of the 22nd International Conference on
Intelligent User Interfaces (IUI '17), Limassol, Cyprus, ACM. 50

51. Thank you!
peterb@pitt.edu
jaewook.ahn@gmail.com
denisparra@gmail.com
k.verbert@tue.nl
51