Targeted advertising reaches users based on various traits,
such as demographics or behaviour. However, users are often
reluctant to accept ads. We hypothesise that users are
more open to targeted advertising if they can inspect, control
and thereby understand the process of ad selection. We
conducted a between-subjects study (N=200) to investigate
to what extent four key aspects of ads (Quality, Behavioural
Intention, Understanding and Attitude) may be affected by
transparency and user control using a flow chart. Our results
indicate that positive effects of flow charts reported from
other domains may also be applicable to advertising: Using
flow charts to provide transparency together with user control
is found to have more positive effects on domain-specfic
quality measures than established, text-based approaches
and using either of the techniques in isolation. The paper
concludes with recommendations for practitioners aiming to
improve user response to ads.
Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts
1. Go With the Flow:
Effects of Transparency and User Control on
Targeted Advertising Using Flow Charts
Yucheng Jin, Karsten Seipp,
Erik Duval✝, Kartrien Verbert
Augment group
HCI @ KU Leuven
9 June 2016
7. 6
importance and potential benefits of transparency
(TR) and user control (UC) for targeted ads.
K. O'Donnell and H. Cramer. People's perceptions of personalized ads. In Proc.
WWW '15 Companion, pages 1293-1298. WWW Steering Committee, 2015.
B. Ur, P. G. Leon, L. F. Cranor, R. Shay, et al. Smart, useful, scary, creepy:
Perceptions of online behavioral advertising. In Proc. SOUPS '12, pages 4:1-4:15.
ACM, 2012.
L. F. Cranor. Can users control online behavioral advertising effectively? Security &
Privacy, IEEE,10(2):93-96, 2012.
• Transparency and user control
10. Positive effects of transparency facilities on trust,
agreement, satisfaction and acceptance of E-Commerce
recommendations.
(Gregor, 1999; Wang, 2007)
9
20. We conducted a between-subjects study on Amazon
Mechanical Turk (MTurk).
- 200 subjects
- $1 for each study
- average time 11 minutes.
We created four experimental conditions:
- Condition 1 (C1): (No-TR & No-UC) base condition
- Condition 2 (C2): (TR & No-UC).
- Condition 3 (C3): (No-TR & UC).
- Condition 4 (C4): (TR & UC)
19
23. Pu, Pearl, Li Chen, and Rong Hu. "A user-centric evaluation framework for recommender systems." Proceedings of the fifth ACM conference
on Recommender systems. ACM, 2011.
• Materials
We used ResQue and tailored the questionnaire to evaluate
four aspects of targeted advertisement:
- Quality
- Behavioral intention
- Understanding
- Attitude
Log file
22
25. • Evaluation steps
1. Introduce web app to subjects
2. Log in to the app with their Facebook accounts.
3. During the trailer, subjects can rate the ads and
configure ads if they wish.
4. After the trailer, subjects were asked to complete the
questionnaire.
24
30. • Quality
29
- Satisfaction: limited (privacy)
- Confidence: limited
- Trust: limited
(company credibility and company trust)
P42 said that “personalized
ads make me feel like spying or a
violation of my privacy.”
R. E. Goldsmith, B. A. Laerty, and S. J. Newell. The impact of corporate credibility and celebrity credibility on consumer
reaction to advertisements and brands. Journal of Advertising, 29(3):43{54, 2000
32. - first implementation of flow charts for targeted ads
- new insights in TR and UC for ads
o Providing only TR improves a user's Behavioral
Intention
o Providing only UC improves a user's Understanding
of the ad selection process
o Providing both TR and UC improves the aspects
Quality, Behavioral Intention, and Understanding
o Attitude does not appear to be affected by either
approach. 31
33. • Limitation
- Studies conducted via MTurk may suffer from
inattentive or “gaming" users.
- A small size of data set of ads. 70 elements of 7 ad
categories.
A. Kittur, E. H. Chi, and B. Suh. Crowdsourcing user studies with mechanical turk. In Proc. CHI '08, pages 453{456. ACM, 2008.
32
34. Thank you for your attention.
Yucheng Jin
yucheng.jin@cs.kuleuven.be
Questions?
IWT (IWT-SBO-Nr. 110067).
Notes de l'éditeur
Most of people hate seeing ads
Fear non-consensual use of their data by third parties
Feel irritated by the same flight ad being shown repeatedly
How can we represent the trade-off between value presented by user data (for instance for the advertiser) and value realized through personalization (for instance of relevant advertisements for the user)?
H ow can we deal with such a trade-off?
One way to achieve this is to experiment with an approach where the user can control what kind of information he shares with which advertiser, and what he expects in return via personalization.
Appear in computer world magazine in 2000
In 2006, the projectVRM was born at Harvard Uni.
Provide tools for individuals to manage relationships with organizations.
Give individuals the ability to share data selectively, without disclosing more personal information than the individual allows.
Give individuals the ability to control how their data is used by others, and for how long.
Several past studies on user perception of targeted advertising
have discussed the importance and benefits of having TR or UC
no comprehensive research exists investigating their capability to improve targeted advertising.
text-based explanations, no interactive visualizations to implement TR and UC
understand the rationale behind recommendations
to fine-tune various recommendations parameters according to their preferences and needs
of other users, tags and suggestions of recommender agents in order to find relevant items
set visualization
increase accuracy and recall in recommendations as well as enhanced user experience.
Simple, visual representation, and intuitive
examine the utility of flowcharts for various purposes
RetroGuide a flowchart-based analytical framework for
query tasks using a step-based approach
flow chart based visualization to show the process of ad selection and a control panel to configure the user model
No opt out
We designed a new visualization based on flow chart to support Transparency (TR) and User control (UC) of targeted ads.
We hypothesize that quality and effectiveness of ads can be increased by empowering users to explore and steer the selection process.
user-centric evaluation framework of recommender system
Playback controls were disabled
Que only displayed after the trailer
to ensure that subjects were exposed to ads
Data was not normally distributed (Shapiro-Wilk, df=50, p<.05)
Kruskal-Wallis
Duun Post hoc
complex and context-dependent
part of a process that is not affected by ad-hoc control and insight.
Illustrate cause and effect of user traits and preferences on ad selection.
from other domains may also be applicable to that of ad
extending the validity and scope of previous findings