Slides of my presentation at IUI 2014, the visual Hybrid Recommender SetFusion - "See What you Want to See: Visual User-Driven Approach for Recommendation" http://dl.acm.org/citation.cfm?id=2557542 DEMO available: http://www.youtube.com/watch?v=9LwSx1V6Yxk

1. See What you Want to See:
Visual User-Driven Approach
for Recommendation
Denis Parra, PUC Chile
Peter Brusilovsky, University of Pittsburgh
Christoph Trattner, Graz University of Technology
IUI 2014, Haifa, Israel

2. Outline
• Short intro to some Challenges in Recommender
Systems
• Our Approach to User Controllability (demo)
• User Study & Results
• Summary & Future Work
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3. INTRODUCTION
Recommender Systems: Introduction & Challenges addressed in this research
3
* Danboard (Danbo): Amazon’s cardboard robot, in these slides represents a recommender system
*

4. Recommender Systems (RecSys)
Systems that help people to find relevant items in a
crowded item or information space (McNee et al. 2006)
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5. Challenges of RecSys Addressed Here
Traditionally, RecSys has focused on producing
accurate recommendation algorithms. In this research,
these challenges are addressed:
1. Human Factors in RecSys: Study controllability by
introducing a novel visualization that presents
fusion of different recommenders
2. Evaluation: Use of Objective,
Subjective & Behavioral metrics
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6. Research Goals & User Studies
Research Goal
• To understand the effect of controllability on the
user engagement and on the overall user
experience of a RecSys
(on this paper) Through
• Two studies conducted using Conference Navigator:
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Program Proceedings Author List Recommendations
http://halley.exp.sis.pitt.edu/cn3/

7. WHYIUISHOULDCARE:HCI+RECSYS
COMMUNITY
Previous research related to this work / Motivating results from TalkExplorer study
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8. TasteWeights (Bostandjev et ala 2012)
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9. Preliminary Work: TalkExplorer
• Adaptation of Aduna Visualization in CN
• Main research question: Do fusion (intersection) of
contexts of relevance improve user experience?
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Center user
CN user
RecommenderRecommender
Cluster
with
intersect
ion of
entities
Cluster (of
talks)
associated
to only one
entity

10. SETFUSION:USER-CONTROLLABLE
HYBRIDINTERFACE
10

11. Our Proposed Interface: SetFusion
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12. Our Proposed Interface - II
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Traditional
Ranked List
Papers sorted by
Relevance.
It combines 3
recommendation
approaches.

13. Our Proposed Interface - III
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Sliders
Allow the user to control the importance of
each data source or recommendation method
Interactive Venn Diagram
Allows the user to inspect and to filter papers
recommended. Actions available:
- Filter item list by clicking on an area
- Highlight a paper by mouse-over on a circle
- Scroll to paper by clicking on a circle
- Indicate bookmarked papers

14. Mixed Hybridization: Item Score
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M: The set of all methods available to fuse
rankreci,mj : rank–position in the list of a recommended item
reci : recommended item i
mj, : recommendation method j
Wmj : weight given by the user to the method mj using the controllable
interface
|Mreci| represents the number of methods by which item reci was
recommended
Slider weight

15. RESEARCH:DETAILS&RESULTS
Description and Analysis of the results of the 3 user studies

16. Studies: CSCW 2013 & UMAP 2013
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CSCW 2013
Conditions Static
List
Interactive
SetFusion
# Attendants ~400
# RecSys
Users
15 22
Study type Between Subjects
UMAP 2013
Interactive
SetFusion
~ 100
50
1 group
Preliminary User study: Here we learned that
the Interactive interface had a positive effect
on user behavior and perception of the recsys
Second study:
Only interactive
interface
CHANGES:
1. Preference Elicitation:
In CSCW we avoided
cold start. In UMAP
we had no constraints
2. Use of the ratings to
update the
recommended items
3. Tuning of Content-
based recommender

17. Comparing CSCW and UMAP
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(Only Interactive Interfaces) CSCW 2013 UMAP 2013
# Users exposed to recommendation 84 95
# Users who used the recommender 22 ( ~ 26 %) 50 ( ~52.6 %)
# Users bookmarked papers 6 ( ~ 27.2 %) 14 (~28 %)
# Talks bookmarked / user avg. 28 / 4.67 103 / 7.36
Average User rating 3.73 / 10 ( ~45.4 %) 3.62 / 8 (~16%)
Usage at Recommender Page
# Talks explored (user avg.) 16.84 14.9
# People returning 7 (~31.8%) 14 (28%)
Average time spent in page (seconds) 261.72 353.8

18. Comparing CSCW and UMAP
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(Only Interactive Interfaces) CSCW 2013 UMAP 2013
# Users exposed to recommendation 84 95
# Users who used the recommender 22 50
# Users bookmarked papers 6 ( ~ 27.2 %) 14 (28 %)
# Talks bookmarked / user avg. 28 / 4.67 103 / 7.36
Average User rating 3.73 / 10 ( ~45.4 %) 3.62 / 8 (~16%)
Usage at Recommender Page
# Talks explored (user avg.) 16.84 14.9
# People returning 7 (~31.8%) 14 (28%)
Average time spent in page (seconds) 261.72 353.8

19. Comparing CSCW and UMAP
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(Only Interactive Interfaces) CSCW 2013 UMAP 2013
# Users exposed to recommendation 84 95
# Users who used the recommender 22 50
# Users bookmarked papers 6 ( ~ 27.2 %) 14 (~28 %)
# Talks bookmarked / user avg. 28 / 4.67 103 / 7.36
Average User rating 3.73 / 10 ( ~45.4 %) 3.62 / 8 (~16%)
Usage at Recommender Page
# Talks explored (user avg.) 16.84 14.9
# People returning 7 (~31.8%) 14 (28%)
Average time spent in page (seconds) 261.72 353.8

20. From the Final Survey
CSCW 2013
(11 users)
UMAP 2013
(8 users)
I don’t think that Conference
Navigator needs a
Recommender System
M = 2.36, S.E. =
0.2
M = 1.5 , S.E. = 0.21
(p < 0.05)
I would recommend this
system to my colleagues
M = 3.36, S.E. =
0.28
M = 4.25, S.E. = 0.33
(p < 0.05)
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- Users perceived SetFusion significantly as a more
useful tool in UMAP than in CSCW

21. CONCLUSIONS&FUTUREWORK

22. Summary of Results
• From Study 1 we showed that User Controllability
had an effect on the user experience with RecSys.
• Comparing SetFusion in Study 1 and Study 2:
– A natural elicitation setting (UMAP) allowed users to
be more engaged on using the system for the task of the
interface: bookmark papers recommended.
– Users also perceived the system as more useful in
UMAP 2013.
– Ratings are a form of giving user control, a big lesson
from Study 1: if you ask user for feedback, use it!
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23. Limitations & Future Work
• Apply our approach to other domains (fusion of
data sources or recommendation algorithms)
• Find alternatives to scale the approach to more than
3 sets, potential alternatives:
– Clustering and
– Radial sets
• Consider other factors that might interact with the
user experience:
– Controllability by itself vs. minimum level of accuracy
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24. THANKS!
QUESTIONS?DPARRA@ING.PUC.CL