boredom-triggered proactive recommendations

Boredom-Triggered Proactive Recommendations

Research

Smarttention Workshop @ MobileHCI ‘15, Copenhagen, Denmark

Martin Pielot

Linas Baltrunas

Nuria Oliver

Attracting attention is essential to many services

SocialMediaCube. Yoel Ben-Avraham. Apr 8, 2013 via Flickr. CC BY-ND 2.0

Average revenue per user in Q1 2014

$7.2

Average revenue per user in Q1 2014

$7.2

$45

Our engagement is now defined by push-driven notifications rather than the traditional pull-driven experience. We’re “hunting and pecking” through our app grid a lot less; the apps that notify us (without over-notifying to the point of uninstall) are rewarded with our engagement (and our dollars).

Proactive Recommendations on Mobiles

30% off of your next beverage ordered at Starbucks in

Diagonal Mar

before 5pm

Not all products have intuitive triggers

by Jakob Nielsen on August 20, 2007

Banner blindness

‚Attention is a limited resource—a person has only so much of it ‘ [Matthew B. Crawford]

Attention Economy: treating human attention as a scarce commodity[Davenport and Beck, 2001]

times square night 2013. chensiyuan. Apr 16, 2013 via Wikipedia. CC BY-SA 4.0

Attention is not always scarce

Show them this photo if someone said technology … . Adam Rifkin. May 21, 2014 via Flickr. CC BY 2.0


Show them this photo if someone said technology … . Adam Rifkin. May 21, 2014 via Flickr. CC BY 2.0

Boredom displeasure caused by “lack of stimulation or inability to be stimulated thereto.” [Fenichel, 1951].

“a bored person is not just someone who does not have anything to do; it’s someone who is actively looking for stimulation but it is unable to do so” [Eastwood, 2002]


Mobile phones are a commonly used tool to fill or kill time when bored [Brown et al. 2014]Show them this photo if someone said technology … . Adam Rifkin. May 21, 2014 via

Flickr. CC BY 2.0



Detecting Boredom from Mobile Phone Usage

UbiComp ’15, Osaka, Japan

Experience SamplingRight now, I feel bored [5-point Likert scale]

ca. 6 times per dayPreferably triggered when phone in use

Borapp

Category Example Feature ExplanationContext Semantic Location Home, work, other, unknown

Demographics Age, gender 38, female

Last Communication Activity

Time last incoming call

Time passed since somebody called the participants

Usage (intensity) Bytes received Number of bytes downloaded in the last 5 minutes

Usage (externally triggered)

Number of notifications

Number of notifications received in the last 5 minutes

Usage (idling) Number of apps Number of apps launched in the last 5 minutes

Usage (type) Most used app App used for the most time in the last 5 minutes.

35 Features, 7 Categories

Data Collection

54 Participants

For two weeks in July 2014Over 40,000,000 usage logs4398 valid self-reports of boredom

0% 20% 40% 60% 80% 100%0%

20%

40%

60%

80%

100%

34.7%42.8%

48.3%52.1%

56.6%62.4%

66.2%70.1%

74.3%76.3%

Recall

Precision

Precision: 70.1% for 30% recall,

62.4% for 50% recall

74.6% AUCROC

Borapp2

Model running on Mobile PhoneUsing primary data set with normalized ground truth and no proneness scores

Constantly predicts when user is bored on the fly

Created Proactive Recommendations

Data Collection

16 Participants

For two weeks in Feb 2015941 Buzzfeed recommendations48% when predicted bored

Click-ratioFraction of times people clicked on notification

8% when not bored20.5% when bored(as inferred by the model)

Difference significantz = -2.102, p = .018

Large effectr = -.543

Engagement-ratioFraction of times people spent more than 30 sec reading

4% when not bored15% when bored(as inferred by the model)

Difference significantz = -2.102, p = .018

Large effectr = -.511

When predicted bored, people were …

More likely to click More likely to read for > 30 seconds

BackgroundRecommendations fuel many of the big, free internet servicesShift from banners (desktop) to proactive recommendations

(mobile)

ProblemAttention is limitedUncontrolled notification / recommendation spamming =>

banner blindness

SolutionWhen bored, attention is not scarce – stimuli seeking emotional

stateUse boredom as trigger for content-independent proactive

recommendations

Contribution from related researchEvidence that boredom can be predicted from mobile phone

usageWhen predicted bored, more open to proactive recommendations

Martin Pielot

Linas Baltrunas

Nuria Oliver

Research

Smarttention Workshop @ MobileHCI ‘15, Copenhagen, Denmark


boredom-triggered proactive recommendations

Science