This document discusses research into personalized and context-aware information access, particularly on mobile devices. It describes how social search engines and mobile applications can help users by tapping into friends' interactions to provide more relevant results. However, it notes that crowds are not always reliable sources of information and that experts may provide more thoughtful evaluations in some domains. The document presents some prototypes developed by Telefonica Research & Development that use social networks and identify domain experts to improve search and recommendation systems.

1. Being Social:
Research in Context-aware and Personalized
Information Access
@ Telefonica
Xavier Amatriain (@xamat)
J.M. Pujol (@solso)
Karen Church (@karenchurch)
#SIGIR2010
Geneva, July '10

2. But first...
About Telefonica and Telefonica R&D

3. Telefonica is a fast-growing Telecom
1989 2000 2008
Clients About 12 About 68 About 260
million million million
subscribers customers customers
Services Basic Wireline and mobile Integrated ICT
telephone and voice, data and solutions for all
data services Internet services customers
Geographies
Operations in Operations in
Spain 25 countries
16 countries
Staff
About 71,000 About 149,000 About 257,000
professionals professionals professionals
Finances Rev: 4,273 M€ Rev: 28,485 M€ Rev: 57,946 M€
EPS(1): 0.45 € EPS(1): 0.67 € EPS: 1.63 €
(1) EPS: Earnings per share

4. Currently among the largest in the world
Telco sector worldwide ranking by market cap (US$ bn)
Source: Bloomberg, 06/12/09

5. Telefonica R&D (TID)
MISSION
“To contribute to the
improvement of the
n Founded in 1988 Telefónica Group’s
n Largest private R&D center in Spain competitivness through
n More than 1100 professionals technological innovation”
n Five centers in Spain and two in Latin America
Telefónica was in 2008 the first Spanish company by R&D Investment and the
third in the EU
Applied R&D
research 61 M€ R&D
Products / Services / Processes 594 M€
development 4.384 M€
Technological Innovation
(1)

6. Scientific Research
Mobile and Ubicomp
Multimedia Core User Modelling &
Data Mining
HCIR
DATA MINING
Wireless Systems
Content Distribution & P2P
Social Networks

7. Enough company talk already...

8. Information Overload

9. More is Less
W
or
se
D
ec
is
ns
io
io
ns
is
ec
D
s
es
L

10. Search engines don’t always hold the answer

12. You are not alone!

13. That's what friends are for...

14. What's her name?

15. Catalan
Adriana

16. Porqpine
J.M. Pujol and P. Rodriguez
"Towards Distributed Social Search Engines", in WWW '09

17. So
ci
e
ar
al
ly
w Personalized
­a
aw
xt
St ar
te
on
an e
C
d­
al
on Lazy collaboration
e
d e
Cost effective
ut
t
ib
is
tr
ex
is
o­
D
c
an
Privacy­preserving C

18. WHAT IS PORQPINE?
●
Distributed social web search engine
●
Locally caches the page & records user interactions
(e.g., bookmarking).
●
Searches by querying local caches of a user’s
friends
●
Pages that friends have “interacted with” are ranked
higher
●
It uses a proxy masking the identity of the friend

20. Context Overload

21. ≠

22. Mobile phones are “personal”

23. Mobile users tend to seek “fresh” content

24. Where is the nearest florist?

25. Where is that really cool cocktail bar
I went to last month?

26. What about discovery?

27. What about information to help me decide?

28. Interesting things close to me?

29. Events near me?

30. Can we improve the search and
discovery experience of mobile
users by providing a readily
available connection to their social
network?

31. SSB (Social Search Browser)
K. Church et al. "SocialSearchBrowser: A novel mobile search and
information discovery tool.", in IUI '10
K. Church et al. “The "Map Trap"? An evaluation of map versus text-based
interfaces for location-based mobile search services”, in WWW '10

32. SSB
iPhone optimized web-
application + Facebook app
When launched it centers on the
users current physical location
Displays all queries/questions
posted by other users in that
location
As users pan/zoom the set of
queries is updated
Users can post new queries or
interact with queries of others

33. Apr 2009, 16 users, 1 week, ireland
Live Field Study in-the-wild
Sept 2009, 34 users, 1 month, ireland

34. A tool for helping and sharing…..

35. A tool for supporting curiosity

36. …..extension to my social network

37. But ...

38. Crowds are not always wise
● Predictions based on large datasets that
are sparse and noisy

39. User Feedback is Noisy
X. Amatriain et al. "I Like It, I Like It Not, Evaluating User Ratings
Noise in Recommender Systems", UMAP '09
● What is the percentage of inconsistencies given an
original rating

40. Who Can we trust?

41. “It is really only experts
who can reliably account
for their reactions”

42. The Wisdom of the Few
X. Amatriain et al. "The wisdom of the few: a collaborative filtering
approach based on expert opinions from the web", SIGIR '09

43. Expert-based CF
● expert = individual that we can trust to have produced
thoughtful, consistent and reliable evaluations (ratings) of
items in a given domain
● Expert-based Collaborative Filtering
● Find neighbors from a reduced set of experts instead of
regular users.
1. Identify domain experts with reliable ratings
2. For each user, compute “expert neighbors”
3. Compute recommendations similar to standard kNN CF

44. Working Prototypes
Music recommendations,
mobile geo-located
recommendations...

45. Summary
➢ We all knew about information overload
➢ But now there is also context overload
(especially on mobile)
➢ One way to deal with both is to tap into the
social network
➢ Many times friends can help but others you
need to move into the crowds
➢ But crowds are not always wise
➢ Sometimes you are better off with experts

46. Thank you!
@xamat