Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Activity-Based Advertising: Techniques and Challenges

2,896 views

Published on

Presentation

  • Be the first to comment

Activity-Based Advertising: Techniques and Challenges

  1. 1. Activity-Based Advertising: Techniques and Challenges Kurt Partridge Bo Begole Ubiquitous Computing Area Palo Alto Research Center, Inc.
  2. 2. Activity Ads People are interested in things they do  Use physical context to infer activity and determine – Topics of interest – Times when person is receptive to information
  3. 3. Activity Advertising motivating vision “An Inconven- Work Transit Store Transit Dinner Transit Email Bed ient Truth” Today: New Phone Graham Crackers PDF Products Plan Activity Japanese Targeted: “Bee Movie” Toyota Prius Restaurant PARC Confidential 3
  4. 4. Activity Stream Example Applications … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Activity … Grocery Movie: “An Work Transit Transit Dinner Inconvenient Truth” Transit Email Bed Store Target Save Minimize Information Energy Waiting • Determine the • Predict departures, • Predict transit user’s needs and destinations, and route and time interests arrivals • Notify to ensure • Help advertisers • Optimize route to “just-in-time” find receptive save fuel arrival at train or consumers • Turn off power to meet a when not in use colleague PARC Confidential 4
  5. 5. Activity by Time of Day how many people do what, and when 100% Miscellaneous Traveling Traveling 90% Telephone Calls performing each activity 80% Percent of population Volunteer Activiti Socializing, Relaxing, Religious and Spir 70% and Leisure Sports, Exercise, a 60% Socializing, Relaxi Education Eating Eating and Drinkin 50% and Government Serv Sleeping / Household Servic 40% Work & Drinking Professional & Pe Personal Care 30% Work-Related Consumer Purcha Education 20% Work & Work-Rel 10% Household Activities Caring For & Help Caring For & Help 0% Household Activities Household Activit Personal Care 0 2 4 6 8 10 12 14 16 18 20 22 Hour of Day This matches our intuition.
  6. 6. Activity Inference a layered architecture Name Data Sources Data Type Format Example Venue Type, PhoneUse, Activity Activity “Restaurant-ing” FriendsActivities Taxonomy Venue Venue Distribution, Type of Specific “Restaurant” Type SpecialPlacesList Venue Location Distribution, “FukiSushi”=0.25, Venue List of Venues VenueDB, Accel, “PizzaChicago”=0.25, Dist. & Probabilities Calendar, Sound “SushiTomo”=0.5 lat=37.402, lon=-122.147, Location Raw Position, GPS Coords + Σ=[0.03, 0.01, 0.01, 0.04], Dist. Accelerometer Uncertainty time=145100 Raw Timestamped lat=37.402305, lon=-122.14769, GPS Position GPS Coords time=145107 PARC Confidential 6
  7. 7. Defining Activity Taxonomy from ATUS 2006 (American Time-Use Survey) Examples of the 18 Examples of the 110 Examples of the 462 Tier 1 Activities Tier 2 Activities Tier 3 Activities Personal Care Sleeping Sleeping Household Activities Grooming Sleeplessness Caring For & Helping Household Members Health-related Self Care Sleeping, n.e.c. Caring For & Helping NonHH Members Personal Activities Work & Work-Related Activities Personal Care Emergencies Interior cleaning Education Personal Care, n.e.c Laundry Consumer Purchases Sewing, repairing, & maintaining textiles Professional & Personal Care Services Housework Storing interior hh items, inc. food … … Housework, n.e.c. PARC Confidential
  8. 8. Time-Use Study Data RESPID TIME ACTIVITY LOCATION Physical care for Respondent’s home 20060101060033 07:00 - 07:20 household children or yard Playing with Respondent’s home 20060101060033 07:20 - 09:20 children, not sports or yard Physical care for Respondent’s home 20060101060033 09:20 - 10:20 household children or yard Travel related to Car, truck, or 20060101060033 10:20 - 10:30 grocery shopping motorcycle (driver) 20060101060033 10:30 - 11:30 Grocery shopping Grocery store ATUS 2006:  263,286 activity episodes  462 activities (Tier 3)  12,943 households  27 different location types PARC Confidential
  9. 9. Activity Prediction Accuracy for different sets of predictor variables Percent Accuracy, Percent Accuracy Duration-Weighted Classifier 0% 0% 20% 40% 40% 60% 60% 80% 80% None Previous Tier 1 activity Previous activity Tier 3 Tier 3 Previous activity & Day of week Previous day Tier 2 Tier 2 Previous activity & Age Group Previous activity & age group Tier 1 Tier 1 Hour of day Location and Time Hour of day & Day of week day of Day correctly Hour of day & Age Group Hour of day & age group Hour of day & Day of week & Age Group Hour of day & day of week & age group predicts activity ~60% of the time. Previous activity & Hour of day Previous hour Location activity & location Previous activity & Location & hour of Location & Hour of day Previousactivity & Location & Hour of day Previous activity & location & hour of day PARC Confidential
  10. 10. Activity Prediction Accuracy at different locations Percent Accuracy, Duration-Weighted Classifier, Percent Accuracy By Location 0% 0% 20% 20% 40% 60% 80% 80% 100% Grocery store Grocery store Transportation Transportation Respondent's workplace Respondent’s workplace Gym, health club Gym, health club At some locations, Other store //mall Other store mall Bank Bank activity is predicted Unspecified place Unspecified place much better than 60%. restaurant //bar Restaurant bar School School Someone else's home Someone else’s home At others, Respondent's home Respondent’s home it’s much worse. Tier 3 1 Place of worship Place of worship Tier 2 Post office Post office Library Library Tier 1 3 Outdoors away from home Outdoors away from home Source: ATUS 2006 PARC Confidential
  11. 11. Predicting Activities from Italian Chinese Learned User Patterns Venue 50% 12:00 Likelihood: 50% 1:00 Weekly Behavior Patterns Context History Monday Tuesda Time Location Visit … … … 11:57- 12:45 37°26’39” 12:00 $ $ -122°9’38” to 1:00 $$ $$ 1:22 - 1:31 37°23’11” Chinese Chinese -122°9’02” Italian Italian … … … … 1:00 to … …
  12. 12. Research Opportunities in the advertising ecosystem Ad Creator user’s ad, bid, placement spec predict activity? interest ad receptivity? stream ad specification? unfamiliarity? Interest Ad Network (e.g. optimal placement? indeterminacy? Modeler Google) incentive balancing? privacy modeling? activity stream ad space details ad GPS  venue visit? venue visit  activity? Activity Ad Space reduce sampling needs? Inferencer Publisher other sensors? sensor data ad When and where is best placement: How to detect Finer-grained activities: Mobile display, ambient Hobbies, exercise, sports, display, content sidebars, …? vacation prefs,

×