This document describes PervADs, a system for pervasive advertising that targets consumers based on their context and activities. PervADs allows direct communication between businesses and consumers. It models consumer context and matches it to advertising offers. Context is represented as a set of dimensions like location, activity, and time. PervADs matches contexts by checking containment and penalizing overly broad or specific contexts. It has been implemented on Android and OpenWRT routers with an ontology for context modeling. PervADs aims to improve on traditional advertising by providing more private and locally-exploited consumer data for more effective targeted ads.

1. PervADs
Semantic Pervasive Advertising
Lorenzo Carrara1
, Giorgio Orsi2
, and Letizia Tanca3
(1) Cubica s.r.l
(2) IFoC, Oxford Martin School, University of Oxford
(3) DEIB, Politecnico di Milano

2. Advertising
A form of communication that creates awareness
of an offer about a product or a service.

3. Advertising
advertiser
consumer
A form of communication that creates awareness
of an offer about a product or a service.

4. Advertising
advertiser
consumer
offer
ads
A form of communication that creates awareness
of an offer about a product or a service.

5. Advertising: key elements
 Targeting
 too broad/too frequent: ads perceived as background noise.
 too focused: you might miss someone.

6. Advertising: key elements
 Targeting
 too broad/too frequent: ads perceived as background noise.
 too focused: you might miss someone.
 Cost
 TV/radio: effective, easy to plan, but expensive.
 billboarding: less effective, harder to plan, not sensibly cheaper.
 Internet: cheap (for now), effective (measurable), but invasive.

7. Pervasive Advertising
Electronic advertising that targets consumers
during their everyday activities.

8. Pervasive Advertising
Electronic advertising that targets consumers
during their everyday activities.
 Targeted and context aware
 profile-based crafting of ads.
 activity-based targeting.

9. Pervasive Advertising
Electronic advertising that targets consumers
during their everyday activities.
 Pervasive
 mobile devices: tablets, smartphones, …, Google glasses
 smart billboards: do not move but that’s pervasive too!
 Targeted and context aware
 profile-based crafting of ads.
 activity-based targeting.

10. Pervasive (and Targeted) Advertising: Issues
 Opacity
 effective [1,2] (>50% thanTV/radio) but debated data.
 in the hands of few companies (Yahoo, Google, Facebook).
 third-parties might bias offers (e.g., Expedia / Booking.com).
[1] Howard Beales. The Value of Behavioral Targeting. NAI.
[2] M. Sala, K. Partridge, L. Jacobson, and J. Begole, “An exploration into activity-informed physical
advertising using pest,” in Proc. of Pervasive, 2007, pp. 73–90.

11. Pervasive (and Targeted) Advertising: Issues
 Privacy
 someone has to know about you and what you do.
 consumers both tracked and profiled (activities, demography,
behaviour).
[1] Howard Beales. The Value of Behavioral Targeting. NAI.
[2] M. Sala, K. Partridge, L. Jacobson, and J. Begole, “An exploration into activity-informed physical
advertising using pest,” in Proc. of Pervasive, 2007, pp. 73–90.
 Opacity
 effective [1,2] (>50% thanTV/radio) but debated data.
 in the hands of few companies (Yahoo, Google, Facebook).
 third-parties might bias offers (e.g., Expedia / Booking.com).

12.  Direct communication between businesses and consumers.
PervADs

13.  Direct communication between businesses and consumers.
 Monitor effectiveness ads campaign
PervADs

14.  Direct communication between businesses and consumers.
 Monitor effectiveness ads campaign.
 Private and local exploitation of richer user data.
PervADs

15. PervADs: A typical scenario

16. PervADs: Structure
Offer
Description
Context
Specification
Human Readable
Advert

17.  Context and data modelling [1]
 CDO (Context Dimension Ontology): context of ads and consumers.
 data about the offer and the product (Schema.org, Good-Relations).
[1] C. Bolchini, C. Curino, G. Orsi, E. Quintarelli, R. Rossato, F. A. Schreiber, L. Tanca: And what can
context do for data? Commun. ACM 52(11): 136-140 (2009)
PervADs

18.  Context matching and reasoning [2]
 matching context of offers and consumers  context containment.
 requires inference.
[1] C. Bolchini, C. Curino, G. Orsi, E. Quintarelli, R. Rossato, F. A. Schreiber, L. Tanca: And what can
context do for data? Commun. ACM 52(11): 136-140 (2009)
[2] G. Orsi, L. Tanca: Context Modelling and Context-Aware Querying - (Can Datalog Be of Help?).
Datalog 2.0. 2010: 225-244
PervADs
 Context and data modelling [1]
 CDO (Context Dimension Ontology): context of ads and consumers.
 data about the offer and the product (Schema.org, Good-Relations).

19. Context Model: Example

20. Context Model: Example
 In summary
 schema  set of FO constraints (DL-Lite)
 instance  set of assignments
 context containment  fact inference

21. Context Inference: Example

22. Context Matching
 Checking only containment is open to cheating.

23. Context Matching
 Checking only containment is open to cheating.
 Penalize contexts that are too broad or too specific.

24. Context Matching
 Penalize contexts that are too broad or too specific.
 per-dimension similarity
user
instance
pervads
instance
 Checking only containment is open to cheating.
,

25. Context Matching
 Penalize contexts that are too broad or too specific.
 per-dimension similarity
user
instance
pervads
instance
and
 Checking only containment is open to cheating.
,

26. Context Matching
 Penalize contexts that are too broad or too specific.
 per-dimension similarity
 aggregate (e.g., avg) over all dimensions.
user
instance
pervads
instance
and
 Checking only containment is open to cheating.
,

27. Dimension Similarity: Example

28. Matching performance vs dimensions

29. Performance vs signal strength

30. PervADs GUI: Queries

31. PervADs GUI: Matching

32.  PervADs core (https://code.google.com/p/pervads/)
 Android client.
 Server application (OpenWRT routers).
Get and develop PervADs-like stuff
 Mobile ontology management (https://code.google.com/p/androjena/)
 AndroJena / μJena.
 ARQoid.
 LucenOid.
 TDBoid.

33. Get and develop PervADs-like stuff
 Mobile ontology management (https://code.google.com/p/androjena/)
 AndroJena / μJena.
 ARQoid.
 LucenOid.
 TDBoid.
Apache license
 PervADs core (https://code.google.com/p/pervads/)
 Android client.
 Server application (OpenWRT routers).

34. Thank you!
http://pervasiveadvertising.org/
More on Pervasive Advertising:
http://www.cs.ox.ac.uk/files/4735/RR-11-07.pdf
More on PervADs: