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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
4
Making existing qualilty content visible
Popular long-tail content
Long-tail content

Quality content
EEXCESS - Objectives
Content enrichment
5
Personalized recommendation
Privacy preservation
EEXCESS  Simplified architecture
Usage
Mining
EEXCESS
Client
Application
Mendeley
Search
Econbiz
Search
Privacy
Proxy
EEXCESS
Federated
Recommender
EEXCESS
6
Privacy Plugin  Recommendations
7
EEXCESS  Privacy objectives
 Transparency
 Show what personal data is being collected
 Show how is this data used
 Control & Feedback
 Over which data is effectively being used
 Granularity of disclosure
 Show the user benefits of disclosure
 Protection
 Ensure user's policy is respected
8
Privacy Plugin  Profile edition
9
Privacy Plugin  Profile data collection
10
 Mendely auth + profile import
Privacy
Proxy
EEXCES
S
Privacy Plugin
11
Usage
Mining
EEXCES
S
Mendeley
Search
Econbiz
Search
Federated
Recommender
EEXCES
S
 Receives
recommendations
 User profile edits
 User browsing activity
 Recommendation requests
Client
Application
 Oauth interactions with
Mendeley
 Recommendations
 Privacy settings
 Profile editing
 Privacy sandbox
Privacy Plugin - Transparency
12
Privacy Plugin  Trancparency
13
Privacy Plugin  Control & Feedback
14
Privacy Plugin  Control & Feedback
15
Privacy Proxy - Protection
16
Usage
Mining
EEXCES
S
Client
Application
Mendeley
Search
Econbiz
Search
Federated
Recommender
EEXCES
S
Privacy
Proxy
EEXCES
S
 Receives
recommendations
 Relays
recommendations
 User profile edits
 User browsing activity
 Recommendation requests
 Stores profiles + browsing activity
 Applies privacy settings
 Relays policy-respectful
recommendation requests
17
Usage
Mining
EEXCES
S
Client
Application
Mendeley
Search
Econbiz
Search
Privacy
Proxy
EEXCES
S
 Returns interleaved
recommendations
 Searches Econbiz
+ Mendeley
 Receives
recommendation requests
Federated
Recommender
EEXCES
S
Basic Federated Recommender
 Receives recommendation request
 User static profile
 User recent activity
 Maps user context into a
weighted term-based query
Demo
18
http://demo.semsaas.com/eexcess-privacy-proxy/
EEXCESS  Privacy challenges
19
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
 Quality
 Recommendations adapted to the user
 Preserving privacy
 Guaranteeing respect of privacy policy
 Scalability
 Ensuring the whole system scales
 Quantifiable measures
20
Contact
@b_habegger
http://www.linkedin.com/in/benjaminhabegger
benjamin.habegger@insa-lyon.fr
Ad

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