Personalization in the Context of
Relevance-Based Visualization
Peter Brusilovsky
Jae-Wook Ahn
Denis Parra
Katrien Verbert...
Outline
• Problem
• History
– InfoCrystall, VIBE, TileBars
• A quest to Adaptive VIBE
– KS-VIBE, QuizVIBE, Adaptive VIBE
•...
Why Relevance Visualization?
• Items might be relevant for a query for
different reasons
– I.e., match different keywords
...
4
InfoCrystal (Spoerri 1993)
From Venn Diagram to IC
6
More "InfoCrystals"
7
VIBE (Korfhage, 1991)
8
TileBars (Hearst, 1995)
9
Towards Adaptive VIBE
• Adaptive nodes
– Social: KS-VIBE
– Knowledge-based: QuizVIBE
• Adaptive topology
– Keyword-based: ...
KS-VIBE (Ahn et al. 2006)
11
12
Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) A two-level adaptive visualization for information access to open-co...
QuizVIBE (2006, Ahn et al.)
Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) QuizVIBE: Accessing Educational Objects ...
• User control in personalized Filtering in ROSETTA project
– Users choose to ranks search results according to user profi...
Adaptive VIBE Idea: Query and UM
for Document Space Separation
15https://www.youtube.com/watch?v=Yt1fMEFlLVA&index=2&list=...
VIBE based query-profile fusion
User Profile Terms
Query Terms
Documents
Mixing user profile and query terms as VIBE POI
VIBE based fusion
Document-query
relevance
Document-query
relevance
Profile-query
relevance
Profile-query
relevance
VIBE based fusion (cont’d)
More about
N. Korean
nuclear weapon
More about
N. Korean
nuclear weapon
More about
Generic
Nucl...
VIBE POI presets
“Circular” preset
“Parallel” preset
• User profile is added on the same playfield
as user query
• Topology is adaptive
• Mediate between profile (green POI) a...
Adaptive VIBE+NE
22
Some Study Results
• A sequence of user studies
– Search vs. VIBE vs. VIBE+NE
• Search -> VIBE -> VIBE+NE offers:
– Better...
Fusing Multiple Relevance
Prospects in Conference Navigator
• Conference Navigator
• TalkExplorer
• SetFusion
• Intersecti...
Relevance in Conference Navigator
• Classic content-based relevance prospects
– Items that has a specific keyword
• Social...
Social Prospect: Details of a talk in CN3
26
Social prospect: User schedules
27
Tag relevance: Tag page in CN3
28
Multiple Recommender Engines
29
Challenge
• Idea: Fuse traditional, social, personal relevance
prospects
• Approach: fuse several relevance lists
– Severa...
John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual
inter...
Related work: TasteWeights
32
Bostandjiev,S.,O'Donovan,J.andHöllerer,T.TasteWeights:avisualinteractivehybrid
recommendersy...
The Approach
• Using set relevance visualization
– One dimension of relevance = one set
• Agent metaphor to mix user- tag-...
TalkExplorer
• Recommendation engines are shown as agents in parallel to users and tags
• Uses Aduna clustermap library: h...
Entity selection
35
Canvas area
36
TalkExplorer
37
overview selected
talks
Interrelations agents and users
38
Evaluation
• Setup
– supervised user study
– 21 participants at UMAP 2012 and ACM Hypertext 2012 conferences
• Results
– T...
SetFusion
• Using set relevance visualization in
the familiar Venn diagram form
– One recommendation source = one
set
• Al...
Brief Results of Two Studies
• SetFusion provides strong engaging effect
– Number of engaged users, bookmarked talks,
expl...
Intersection Explorer
• Based on ideas of
Talk Explorer
• New approach for
scalable multi-set
visualization
• Try it yours...
Intersection Explorer at IUI2017
49
http://halley.exp.sis.pitt.edu/cn3/iestudy3.php?conferenceID=148
Readings
• Ahn, J.-w., Farzan, R., and Brusilovsky, P. (2006) A two-level adaptive visualization for information access to...
Thank you!
peterb@pitt.edu
jaewook.ahn@gmail.com
denisparra@gmail.com
k.verbert@tue.nl
51
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
Personalization in the Context of Relevance-Based Visualization
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Personalization in the Context of Relevance-Based Visualization

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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

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  • Related work: peerchooser John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual interactive recommendation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08).
  • Personalization in the Context of Relevance-Based Visualization

    1. 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. 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. 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. 4
    5. 5. InfoCrystal (Spoerri 1993)
    6. 6. From Venn Diagram to IC 6
    7. 7. More "InfoCrystals" 7
    8. 8. VIBE (Korfhage, 1991) 8
    9. 9. TileBars (Hearst, 1995) 9
    10. 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. 11. KS-VIBE (Ahn et al. 2006) 11
    12. 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. 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. 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. 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. 16. VIBE based query-profile fusion User Profile Terms Query Terms Documents Mixing user profile and query terms as VIBE POI
    17. 17. VIBE based fusion Document-query relevance Document-query relevance Profile-query relevance Profile-query relevance
    18. 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. 19. VIBE POI presets “Circular” preset “Parallel” preset
    20. 20. • 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
    21. 21. Adaptive VIBE+NE 22
    22. 22. 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
    23. 23. Fusing Multiple Relevance Prospects in Conference Navigator • Conference Navigator • TalkExplorer • SetFusion • Intersection Explorer 24
    24. 24. 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.
    25. 25. Social Prospect: Details of a talk in CN3 26
    26. 26. Social prospect: User schedules 27
    27. 27. Tag relevance: Tag page in CN3 28
    28. 28. Multiple Recommender Engines 29
    29. 29. 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
    30. 30. John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual interactive recommendation. CHI '08 Related work: PeerChooser 31
    31. 31. 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.
    32. 32. 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
    33. 33. TalkExplorer • Recommendation engines are shown as agents in parallel to users and tags • Uses Aduna clustermap library: http://www.aduna-software.com/ 34
    34. 34. Entity selection 35
    35. 35. Canvas area 36
    36. 36. TalkExplorer 37 overview selected talks
    37. 37. Interrelations agents and users 38
    38. 38. 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
    39. 39. 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
    40. 40. 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
    41. 41. 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.
    42. 42. Intersection Explorer at IUI2017 49 http://halley.exp.sis.pitt.edu/cn3/iestudy3.php?conferenceID=148
    43. 43. 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
    44. 44. Thank you! peterb@pitt.edu jaewook.ahn@gmail.com denisparra@gmail.com k.verbert@tue.nl 51

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