Recent developments in the area of social networking have lead to prominent users leaving facebook due to privacy concerns. In order to really understand what motivated facebook to implement these controversial changes, you have to look at the future of recommender systems. I will introduce my current research in the areas of multi-source, cross-domain and privacy enabled user profiling and recommendation, and show how it relates to current developments in the social networking space.

1. Digital Enterprise Research Institute www.deri.ie
What your hairstyle says about your political
preferences,
and why you should care about the future of
recommender systems
Benjamin Heitmann
Unit for Information Mining and Retrieval (UIMR)
Funded by Science Foundation Ireland under
Grant No. SFI/08/CE/I1380 (Líon-2)
 Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
Chapter

2. Why has Facebook been so controversial?
Digital Enterprise Research Institute www.deri.ie
Benjamin.Heitmann
slide 2 of 17
@deri.org

3. Why has Facebook been so controversial?
Digital Enterprise Research Institute www.deri.ie
 new Facebook features
in April 2010
 introduced new default
privacy settings
 from private to public
by default
Benjamin.Heitmann
slide 2 of 17
@deri.org

4. Why has Facebook been so controversial?
Digital Enterprise Research Institute www.deri.ie
 new Facebook features
in April 2010
 introduced new default
privacy settings
 from private to public
by default
 huge backlash in the
media
Benjamin.Heitmann
slide 2 of 17
@deri.org

5. Why has Facebook been so controversial?
Digital Enterprise Research Institute www.deri.ie
 new Facebook features
in April 2010
 introduced new default
privacy settings
 from private to public
by default
 huge backlash in the
media
 Result: simplified
privacy defaults
Benjamin.Heitmann
slide 2 of 17
@deri.org

6. Why has Facebook been so controversial?
Digital Enterprise Research Institute www.deri.ie
 new Facebook features
in April 2010
 introduced new default
privacy settings
 from private to public
by default
 huge backlash in the
media
 Result: simplified
privacy defaults
 Why change defaults in
the first place?
 Advertisements are
recommendations, they
need user data!
Benjamin.Heitmann
slide 2 of 17
@deri.org

7. Recommendations have become a
commodity!
Digital Enterprise Research Institute www.deri.ie
 Users expect “smart” web sites
 Recommendations are part of such a user experience
Benjamin.Heitmann
slide 3 of 17
@deri.org

8. So why should you care?
Digital Enterprise Research Institute www.deri.ie
 Recommendations
are a commodity:
they are required
for online products
Facebook Open Graph protocol
 coming paradigm
shifts:
recommendations
outside the context of
a single site
much more
interesting / scary
recommendations
Benjamin.Heitmann
slide 4 of 17
@deri.org

9. The challenge
Digital Enterprise Research Institute www.deri.ie
 Problem: How to compete
with existing systems?
 provide relevant results
beyond your domain
 share user profile data
beyond your site
 encourage users to
trust you
 Common aspect: vs.
 multiple parties
 eco-systems
 architecture is new
required to define startup established
roles, standards and recommendation
interaction services
Benjamin.Heitmann
slide 5 of 17
@deri.org

10. Overview
Digital Enterprise Research Institute www.deri.ie
 Background:
How are recommendations made?
 Multi-Source recommendations:
Going beyond the context of a site
 Privacy-enabled profiles:
Enabling the post-facebook future
 Cross-domain recommendations:
So good, its almost scary
Benjamin.Heitmann
slide 6 of 17
@deri.org

11. Background: adaptive personalisation
Digital Enterprise Research Institute www.deri.ie
closed recommender system open recommender system
input data input data
recommendation recommendation
algorithm algorithm
background data integrated background data
external data sources source 1 source n
. . . .
 Adaptive system: personalisation is based on an explicit user
model and profile data
 3 components: Rec. algorithm uses background and input data
to make recommendations
 Most existing rec. systems rely on collection of
in-house data and do not use external data
Benjamin.Heitmann
slide 7 of 17
@deri.org

12. Multi-source recommendations:
Digital Enterprise Research Institute www.deri.ie
 First paradigm shift:
use background data
from external sources
link their items to your
users
 extend the context of
the recommendations
beyond your site
 Challenge: how to use
Linking the Facebook
Social Graph to structured data as
restaurants on Yelp
recommendation input
 Solution:
use Linked Data
Benjamin.Heitmann
slide 8 of 17
@deri.org

13. Prototype: using Linked Data for multi-
source recommendations
Digital Enterprise Research Institute www.deri.ie
foaf:Person
myspace:topFriend
mo:MusicalArtist foaf:Person foaf:Person URIs
foaf:Document URIs
myspace (via DBTune) 0 1 0 1 1 0 1
0 0 1 0 1 0 1
foaf:interest
....
foaf:Document
foaf:Document
sioc:links_to
user-item matrix
FOAF vocabulary
sioct:WikiArticle
wikipedia (via SIOC exporter)
integrate with transform
apply collaborative
SPARQL CONSTRUCT from RDF graph
ltering algorithm on
query to matrix
user-item matrix
 Use Linked Data to augment private data
 this enables multi-source recommendations:
 recommend new items through external background data
 reduce sparsity by adding connections from ext. backg. data
 provide recommendations for new users by using an external profile
Benjamin.Heitmann
slide 9 of 17
@deri.org

14. Evaluation of multi-source
recommendation results
Digital Enterprise Research Institute www.deri.ie
 Smart Radio: first online
streaming and rec. radio
 small: 190 users and 330 musicians binary cosine similarity
i1, i2: items e.g. musical artists
 create links to MySpace artists
via DBpedia
 external background data: 11000 relevant recommendations
users, 25000 new connections
 collaborative filtering with
binary cosine similarity
 evaluation compared to
Last.FM as “gold standard”
 improve precision: 2% -> 14%
 improve recall: 7% -> 33%
Benjamin.Heitmann
slide 10 of 17
@deri.org

15. Data sharing for recommendations
Digital Enterprise Research Institute www.deri.ie
 2nd paradigm shift:
sharing user profile
data between sites
 Reverse perspective:
?
users want to move
between social
networks!
data
 Primary user
recommendations
sharing concern: privacy
 Surprise: really hot
topic from this
perspective
Benjamin.Heitmann
slide 11 of 17
@deri.org

16. Facebook approach to privacy-enabled
user profiles
Digital Enterprise Research Institute www.deri.ie
 The Facebook
approach:
express
preference  centralised user
authentication
for user action
profile
 data sharing for e.g.
web site
interaction recommendations
cross domain  closed system
data sharing
if authorised  no portability at
by user
all!
 Challenge: open
alternative with
portability and
recommendations for privacy!
external site provided by
(at the same time)
facebook
Benjamin.Heitmann
slide 12 of 17
@deri.org

17. Alternative: architecture for private and
portable user profiles
Digital Enterprise Research Institute www.deri.ie
 User profile:
 Profile data expressed WebID
using RDF (FOAF+SIOC)
 WebID provides identity
private key public key
(2 parts)
– private SSL Key in user
agent
– public SSL Key in FOAF user agent
FOAF Profile
profile
 Roles: stored
 user agents: manage user in
identities
 profile storage service:
retrieves user profile
stores 1 or many profiles if user authorises it profile storage site
data consumer
 data consumers: provide
services for users
Benjamin.Heitmann
slide 13 of 17
@deri.org

18. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
WebID
private key public key
Storage URI
user agent
FOAF Profile
stored
in
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

19. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
Storage URI
user agent
FOAF Profile
stored
in
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

20. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
user agent
FOAF Profile
Any nice stored
restaurants? in
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

21. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
2. Chow gets profile storage via
user agent
Firefox FOAF Profile
Firefox stored
provides in
storage URI
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

22. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
2. Chow gets profile storage via
user agent
Firefox FOAF Profile
3. Chow redirects Firefox to
stored
Openbook for authorisation redirect to
openbook in
for authorisation
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

23. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
2. Chow gets profile storage via
user agent
Firefox FOAF Profile
3. Chow redirects Firefox to User authorises stored
Openbook for authorisation Openbook to in
show parts of
4. User authorises Openbook to profile to Chow
show some profile parts to Chow
profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

24. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
2. Chow gets profile storage via
user agent
Firefox FOAF Profile
3. Chow redirects Firefox to
stored
Openbook for authorisation in
4. User authorises Openbook to
show some profile parts to Chow
5.Openbook redirects to Chow
redirect back to Chow profile storage site
data consumer
Benjamin.Heitmann
slide 14 of 17
@deri.org

25. Communication pattern of the proposed
architecture
Digital Enterprise Research Institute www.deri.ie
 Scenario: restaurant
recommendation WebID
 Assumption: user is logged into
Openbook
private key public key
1. User requests nice restaurants Storage URI
from Chow
2. Chow gets profile storage via
user agent
Firefox FOAF Profile
3. Chow redirects Firefox to
stored
Openbook for authorisation in
4. User authorises Openbook to Chow retrieves profile
show some profile parts to Chow parts now
5.Openbook redirects to Chow
profile storage site
6.Now Chow accesses parts of data consumer
profile data on openbook
Benjamin.Heitmann
slide 14 of 17
@deri.org

26. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
Benjamin.Heitmann
slide 15 of 17
@deri.org

27. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
Benjamin.Heitmann
slide 15 of 17
@deri.org

28. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

29. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

30. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

31. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

32. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

33. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
Benjamin.Heitmann
slide 15 of 17
@deri.org

34. Cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 3rd paradigm shift:
Provide relevant results
beyond your domain
 Requires user data
from multiple
domains
 Hunch.com shows
one solution: ask
your users
 Alternative
solution:
automatically link
domains and
communities
Benjamin.Heitmann
slide 15 of 17
@deri.org

35. Future work: using Linked Data for
cross-domain recommendations
Digital Enterprise Research Institute www.deri.ie
 Exploit the intrinsic
links between
John Cage Johnny Cash Elvis Metallica sources:
Myspace  links between data
domain: music
from different sources
KyleButler TheTeacher
 connections between
FOAF possible different domains
profile recommendations
 identical users in
Dexter
Morgan different communities
Wikipedia
many domains
Country: City: Sport:  Requires links between
Netherlands Amsterdam Sailing
data sources
(a.k.a. “The Linkage
Problem)
Benjamin.Heitmann
slide 16 of 17
@deri.org

36. Summary
Digital Enterprise Research Institute www.deri.ie
 Recommendations have become a commodity
 they are required for a good user experience
 3 coming paradigm shifts:
 Go beyond the context of one site
(multi-source recommendations)
 Provide results beyond your primary domain
(cross-domain recommendations)
 Enable eco-systems built around portable user profiles
(Privacy-enabled user profile portability)
 Developing eco-systems with multiple parties
requires an architecture
(mutual agreement on roles, standards and
communication patterns)
Benjamin.Heitmann
slide 17 of 17
@deri.org