activity-based advertising:techniques and challenges
DESCRIPTION
PresentationTRANSCRIPT
Activity-Based Advertising:Techniques and Challenges
Kurt Partridge
Bo Begole
Ubiquitous Computing Area
Palo Alto Research Center, Inc.
Activity AdsPeople are interested in things they do
Advertising
Advertising
Physical
ActivitiesPhysical
Activities
Use physical context to infer activity and determine– Topics of interest– Times when person is receptive to information
PARC Confidential 3
Finding
Nemo
Activity Advertisingmotivating vision
Work Transit Store Transit Dinner“An Inconven-
ient Truth”Transit Email Bed
Toyota PriusJapanese
Restaurant“Bee Movie”
PDF ProductsGraham CrackersNew Phone
PlanToday:
ActivityTargeted:
PARC Confidential 4
Activity Stream Example Applications
Work Transit GroceryStore Transit Dinner Movie: “An
Inconvenient Truth” Transit Email Bed
… Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity …
Save Energy
10% Off
Target Information
Minimize Waiting
• Predict transit route and time
• Notify to ensure “just-in-time” arrival at train or to meet a colleague
• Predict departures, destinations, and arrivals
• Optimize route to save fuel
• Turn off power when not in use
• Determine the user’s needs and interests
• Help advertisers find receptive consumers
Activity by Time of Dayhow many people do what, and when
0%10%20%30%40%50%60%70%80%90%
100%
0 2 4 6 8 10 12 14 16 18 20 22
Perc
ent o
f pop
ulati
on
perf
orm
ing
each
acti
vity
Hour of Day
MiscellaneousTravelingTelephone CallsVolunteer ActivitiesReligious and Spiritual ActivitiesSports, Exercise, and RecreationSocializing, Relaxing, and LeisureEating and DrinkingGovernment Services & Civic ObligationsHousehold ServicesProfessional & Personal Care ServicesConsumer PurchasesEducationWork & Work-Related ActivitiesCaring For & Helping NonHH MembersCaring For & Helping Household MembersHousehold ActivitiesPersonal Care
Sleeping / Personal Care
Household Activities
Work &Work-Related
Traveling
Socializing, Relaxing,and Leisure
Eatingand
Drinking
Education
Household Activities
This matches our intuition.
Activity Inferencea layered architecture
Name Data Sources Data Type Format Example
Activity Venue Type, PhoneUse, FriendsActivities
Activity Taxonomy “Restaurant-ing”
Venue Type
Venue Distribution, SpecialPlacesList
Type of Specific Venue “Restaurant”
Venue Dist.
Location Distribution, VenueDB, Accel, Calendar, Sound
List of Venues & Probabilities
“FukiSushi”=0.25, “PizzaChicago”=0.25,
“SushiTomo”=0.5
Location Dist.
Raw Position, Accelerometer
GPS Coords +Uncertainty
lat=37.402, lon=-122.147, Σ=[0.03, 0.01, 0.01, 0.04],
time=145100
Raw Position GPS Timestamped
GPS Coordslat=37.402305, lon=-122.14769,
time=145107
PARC Confidential 6
Defining Activity
Taxonomy from ATUS 2006 (American Time-Use Survey)
Personal Care Household Activities Caring For & Helping Household Members Caring For & Helping NonHH Members Work & Work-Related ActivitiesEducation Consumer Purchases Professional & Personal Care Services …
SleepingGroomingHealth-related Self CarePersonal ActivitiesPersonal Care EmergenciesPersonal Care, n.e.c
Housework…
SleepingSleeplessnessSleeping, n.e.c.
Interior cleaningLaundrySewing, repairing, & maintaining textilesStoring interior hh items, inc. foodHousework, n.e.c.
Examples of the 18Tier 1 Activities
Examples of the 110Tier 2 Activities
Examples of the 462Tier 3 Activities
PARC Confidential
Time-Use Study Data
RESPID TIME ACTIVITY LOCATION
20060101060033 07:00 - 07:20Physical care for household children
Respondent’s home or yard
20060101060033 07:20 - 09:20Playing with children, not sports
Respondent’s home or yard
20060101060033 09:20 - 10:20Physical care for household children
Respondent’s home or yard
20060101060033 10:20 - 10:30Travel related to grocery shopping
Car, truck, or motorcycle (driver)
20060101060033 10:30 - 11:30 Grocery shopping Grocery store
263,286 activity episodes 12,943 households
462 activities (Tier 3) 27 different location types
PARC Confidential
ATUS 2006:
Activity Prediction Accuracy for different sets of predictor variables
0% 20% 40% 60% 80%
None
Previous Tier 1 activityPrevious activity & Day of week
Previous activity & Age Group
Hour of dayHour of day & Day of week
Hour of day & Age GroupHour of day & Day of week & Age Group
Previous activity & Hour of day
LocationPrevious activity & Location
Location & Hour of dayPrevious activity & Location & Hour of day
Percent Accuracy, Duration-Weighted Classifier
Tier 3
Tier 2
Tier 1
Percent Accuracy
None
Previous Tier 1 activityPrevious activity & day of week
Previous activity & age group
Hour of dayHour of day & day of week
Hour of day & age groupHour of day & day of week & age group
Previous activity & hour of day
LocationPrevious activity & location
Location & hour of dayPrevious activity & location & hour of day
Tier 3
Tier 2
Tier 1
0% 20% 40% 60% 80%
PARC Confidential
Location and Time of Day correctly predicts activity
~60% of the time.
Activity Prediction Accuracy at different locations
0% 20% 40% 60% 80% 100%
Outdoors away from homeLibrary
Post officePlace of worship
Respondent's homeSomeone else's home
Schoolrestaurant / bar
Unspecified placeBank
Other store / mallGym, health club
Respondent's workplaceTransportation
Grocery store
Tier 1
Tier 2
Tier 3
Percent Accuracy, Duration-Weighted Classifier, By Location
Percent Accuracy
Tier 3
Tier 2
Tier 1
0% 20% 40% 60% 80% 100%
Grocery store
TransportationRespondent’s workplace
Gym, health clubOther store / mall
BankUnspecified place
Restaurant / barSchool
Someone else’s homeRespondent’s home
Place of worshipPost office
LibraryOutdoors away from home
PARC Confidential
At some locations, activity is predicted
much better than 60%.
At others, it’s much worse.
Source: ATUS 2006
50%
50%Venue
Likelihood: 1:00
Monday Tuesda
…
12:00 to 1:00
1:00 to
12:00
Time Location Visit
11:57- 12:45 37°26’39”-122°9’38”
1:22 - 1:31 37°23’11”-122°9’02”
… … …
Context History
Weekly Behavior Patterns
$$$
ChineseItalian
……
…$
$$
ChineseItalian
…
…
Predicting Activities fromLearned User Patterns
ChineseItalian
Research Opportunitiesin the advertising ecosystem
Activity Inferencer
Activity Inferencer
Interest Modeler
Interest Modeler
Ad Network (e.g. Google)
Ad Network (e.g. Google)
Ad Space Publisher
Ad Space Publisher
Ad CreatorAd Creator
sensor data
activity stream
user’s interest stream
ad, bid, placement spec
ad
ad
ad space details
GPS venue visit?venue visit activity?
reduce sampling needs?other sensors?
predict activity? ad receptivity?
unfamiliarity?indeterminacy?
privacy modeling?
ad specification?optimal placement?incentive balancing?
How to detect Finer-grained activities:
Hobbies, exercise, sports, vacation prefs,
When and where is best placement:
Mobile display, ambient display, content sidebars, …?