Acceptance of Highly-Personalized Ads

Users often perceive ads annoying (when they are unrelated to their interests), or on the other hand they find ads creepy or scary (when matched to their interests and activities). Assuming the recommendation systems will become able to better and better match ads to our interests, how will the users react?

Many apps and internet services are free-to-use. The cost of developing and running these services is paid by bringing advertisement to our attention. However, ads are often annoying an unrelated to our interests.

Hence, services collect more and more information about us to better target and personalize those ads. While research shows that personalized ads can be less annoying and more effective, they require the logging of our demographics and our behavior, such as our searches, the content we browse, or our whereabouts and movements.

Personalization has raised concerns: users worry about the personal data that is being used to create personalized ads. In previous studies, when given a choice, many people expressed hesitation to share information: their concerns of sharing personal data –such as browsing and location history– outweigh the perceived usefulness of personalized ads.

If such concerns persist, investing effort into further personalizing ads would not be worthwhile, as people would not accept them.

We therefore set out to explore the research question: “would people be willing to share their personal data in exchange for highly-personalized online ads?”

To answer this question, we conducted a so called Wizard-of-Oz deception study, i.e., a study in which we simulated a system that can generate highly-personalized ads. Our volunteers were exposed via a web browser to three different highly-personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits.

The participants’ reactions were surprisingly favorable:

  • in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions;
  • almost 90% of participants would share at least two of the three data sources with advertisers; and
  • about 50% would share all data sources.

Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavor.

The work was presented at ACM UMAP ’17, the ACM International Conference on User Modelling, Adaptation and Personalization, which took place in July 2017 in Bratislava, Slovakia.

Citation :
“OMG! How did it know that?” Reactions to Highly-Personalized Ads.
Aleksandar Matic, Martin Pielot, Nuria Oliver
UMAP ’17 (Adjunct): ACM International Conference on User Modelling, Adaptation and Personalization, 2017.

(pdf)

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Are You Prone to be Bored? Your Phone Can Tell

This work is a follow-up project on our research on When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage

People using their mobile phone in the metro to kill time.
People using their mobile phone in the metro to kill time.

We might think that technology has solved the problem of boredom. More and more devices provide us with an ample source of entertainment at our fingertips.

Paradoxically, today we appear to be more prone to boredom than ever before. The explanation might be that over time people habituate to an increasing exposure to stimuli such that, when the level of stimulation drops, they become bored.

In an extension on our study of detecting phases of boredom from mobile phone usage, in this study, we (Aleksandar Matic, Nuria Oliver, and me of the Scientific Group of Telefonica) explored to what extent technology use is intertwined with boredom proneness, and whether the level of boredom proneness can be inferred from it. We collected data on the accumulated daily mobile phone usage patterns of 22 volunteers, such as, the average number of apps started in a day or the variance of the amount of notifications received per day. Then, those participants filled our the standardized Boredom Proneness Scale.

We found that daily usage patterns can estimate whether the person is above-average prone to boredom with an accuracy of over 80%. Individuals with high boredom proneness were having more unstable daily phone usage patterns: they launched a higher number of apps per day, had strong peaks of social network activity, and turned on the phone a lot. However, surprisingly, the overall time of using the phone was not higher than for individuals with lower boredom proneness.

Boredom proneness is related to a number of negative outcomes, such as depression, drug & alcohol consumption, or anxiety. Obtaining boredom proneness in an unobtrusive, automatic way can, amongst other things, help in the adjustment of the treatments of such health issues.

The work was presented in September 2015 at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, which took place in Osaka, Japan.

The details of the work are described in
Boredom-Computer Interaction: Boredom Proneness and The Use of Smartphone
Aleksandar Matic, Martin Pielot, Nuria Oliver
UbiComp’ 15: ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.

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When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage (UbiComp ’15)

In times of information overload, attention has become a limiting factor in the way we consume information. Hence, researchers suggested to treat attention as a scarce resource coined the phrase attention economy. Given that attention is also what pays the bills of many free internet services through ads, some even speak of the Attention War. Soon, this war may start extending to our mobile devices, where already today, apps try to engage you through proactive push notifications.

bored

Yet, attention is not always scarce. When being bored, attention is abundant, and people often turn to their phones to kill time. So, wouldn’t it be great if more services sought your attention when you are bored and left you alone when you were busy?

Since mobile phones are often used to kill time, we — that’s Tilman Dinger from the hciLab of the University of Stuttgart, and Jose San Pedro Wandelmer, Nuria Oliver, and me from Telefonica’s scientific group — saw an opportunity in detecting those moments automatically. If phones knew when their users are killing time, maybe they could suggest them to make better use of the moment.

To identify, which usage patterns are indicative for boredom, we logged phone usage patterns of 54 volunteers for 2 weeks. At the same time, we asked them to frequently report how bored they felt. We found that patterns around the recency of communication activity, context, demographics, and phone usage intensity were related to boredom.

esm

These patterns allow us to create a model that predicts when a person is more bored than usual with an AUCROC of 74.5%. It achieves a precision of over 62%, when its sensitivity is tuned detecting 50% of the boredom episodes.

precrecallfull

While this is far from perfect, we proved its effectiveness in a follow-up study: we created an app (available on Google Play, more info here) that, at random times, created notifications, which suggest to read news articles.

buzzfeednotification

When predicted bored, the participants opened those articles in over 20% of the cases and kept reading the article for more than 30 seconds in 15% of the cases. In contrast, when they were not bored, they opened the article in only 8% of the cases and kept reading it for more than 30 seconds in only 4% of the cases.
buzzfeedresults
Statistical analysis shows that the predicting accounts for significant share of the observed increase.

While we certainly don’t feel that recommending Buzzfeed articles will be the cure peoples’ boredom, at least not for the majority of them, the study provides evidence that the prediction works.

Now how can mobile phones better serve users, when they can detect phases of boredom? We see four application scenarios:

  • Engage users with relevant contents to mitigate boredom,
  • Shield users from non-important interruptions when not bored,
  • Propose useful but not necessarily boredom-curing activities, such as clearing a backlog of To Do’s or revisiting vocabulary lists, and
  • Suggest to stop killing time with the phone and embrace boredom, as it is essential to creative processes and self-reflection.

Relatedly to this work, in a follow-up study, we also showed that mobile phones can predict the boredom proneness, the predisposition of experiencing boredom.

The work was presented in September 2015 at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, taking place in Osaka, Japan, where it received best-paper award.

When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage.
Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver
UbiComp’ 15: ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.

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