eexcess blend-privacy-recommendation
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EEXCESS : Privacy-respectful content recommendation
Benjamin Habegger, INSA Lyon
EEXCESS Project
Enhancing Europes eXchange in Cultural Educational and Scientific reSources.
http://eexcess.eu/
EEXCESS Partners
EEXCESS Main problem
Making existing qualilty content visible
Popular long-tail content
Long-tail contentQuality content
EEXCESS - Objectives
Content enrichment
Personalized recommendation
Privacy preservation
EEXCESS Simplified architecture
UsageMiningEEXCESS
ClientApplication
MendeleySearch
EconbizSearch
PrivacyProxyEEXCESS
FederatedRecommenderEEXCESS
Privacy Plugin Recommendations
EEXCESS Privacy objectives
TransparencyShow what personal data is being collected
Show how is this data used
Control & FeedbackOver which data is effectively being used
Granularity of disclosure
Show the user benefits of disclosure
ProtectionEnsure user's policy is respected
Privacy Plugin Profile edition
Privacy Plugin Profile data collection
Mendely auth + profile import
PrivacyProxyEEXCESS
Privacy Plugin
UsageMiningEEXCESS
MendeleySearch
EconbizSearch
FederatedRecommenderEEXCESS
Receives
recommendations
User profile edits
User browsing activity
Recommendation requests
ClientApplication
Oauth interactions with
Mendeley
Recommendations
Privacy settings
Profile editing
Privacy sandbox
Privacy Plugin - Transparency
Privacy Plugin Trancparency
Privacy Plugin Control & Feedback
Privacy Plugin Control & Feedback
Privacy Proxy - Protection
UsageMiningEEXCESS
ClientApplication
MendeleySearch
EconbizSearch
FederatedRecommenderEEXCESS
PrivacyProxyEEXCESS
Receives
recommendations
Relays
recommendations
User profile edits
User browsing activity
Recommendation requests
Stores profiles + browsing activity
Applies privacy settings
Relays policy-respectfulrecommendation requests
UsageMiningEEXCESS
ClientApplication
MendeleySearch
EconbizSearch
PrivacyProxyEEXCESS
Returns interleaved
recommendations
Searches Econbiz
+ Mendeley
Receivesrecommendation requests
FederatedRecommenderEEXCESS
Basic Federated Recommender
Receives recommendation requestUser static profile
User recent activity
Maps user context into aweighted term-based query
Demo
http://demo.semsaas.com/eexcess-privacy-proxy/
EEXCESS Privacy challenges
D
...
Paul
What is the average age ?
38
Paul is 2 years less than the average person in D
External Knowledge
36
EEXCESS Privacy challenges
QualityRecommendations adapted to the user
Preserving privacyGuaranteeing respect of privacy policy
ScalabilityEnsuring the whole system scales
Quantifiable measures
Contact
@b_habegger
http://www.linkedin.com/in/benjaminhabegger
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Seventh Outline LevelVortragende/r, Ort, Datum
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Zweite Ebene
Dritte Ebene
Vierte Ebene
Fnfte Ebene
10/2/13
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Seventh Outline LevelKontaktdaten der Vortragenden/
des Vortragenden
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Seventh Outline Levelwww.eexcess.eu