Transcript
PervADsSemantic 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
Advertising
A form of communication that creates awareness of an offer about a product or a service.
Advertising
advertiserconsumer
A form of communication that creates awareness of an offer about a product or a service.
Advertising
advertiserconsumer
offer
ads
A form of communication that creates awareness of an offer about a product or a service.
Advertising: key elements
Targeting
too broad/too frequent: ads perceived as background noise.
too focused: you might miss someone.
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.
Pervasive Advertising
Electronic advertising that targets consumers during their everyday activities.
Pervasive Advertising
Electronic advertising that targets consumers during their everyday activities.
Targeted and context aware
profile-based crafting of ads.
activity-based targeting.
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.
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.
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).
Direct communication between businesses and consumers.
PervADs
Direct communication between businesses and consumers.
Monitor effectiveness ads campaign
PervADs
Direct communication between businesses and consumers.
Monitor effectiveness ads campaign.
Private and local exploitation of richer user data.
PervADs
PervADs: A typical scenario
PervADs: Structure
OfferDescription
ContextSpecification
Human ReadableAdvert
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
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).
Context Model: Example
Context Model: Example
In summary
schema set of FO constraints (DL-Lite)
instance set of assignments
context containment fact inference
Context Inference: Example
Context Matching
Checking only containment is open to cheating.
Context Matching
Checking only containment is open to cheating.
Penalize contexts that are too broad or too specific.
Context Matching
Penalize contexts that are too broad or too specific.
per-dimension similarity
userinstance
pervadsinstance
Checking only containment is open to cheating.
,
Context Matching
Penalize contexts that are too broad or too specific.
per-dimension similarity
userinstance
pervadsinstance
and
Checking only containment is open to cheating.
,
Context Matching
Penalize contexts that are too broad or too specific.
per-dimension similarity
aggregate (e.g., avg) over all dimensions.
userinstance
pervadsinstance
and
Checking only containment is open to cheating.
,
Dimension Similarity: Example
Matching performance vs dimensions
Performance vs signal strength
PervADs GUI: Queries
PervADs GUI: Matching
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.
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).
Thank you!
http://pervasiveadvertising.org/More on Pervasive Advertising:
http://www.cs.ox.ac.uk/files/4735/RR-11-07.pdfMore on PervADs:
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