Keynote talk at the 3rd International Workshop on Social Recommender Systems In conjunction with UMAP, Montreal, Canada, July 17, 2012

1. The Power of Known
Peers: A Study in Two
Domains
Peter Brusilovsky with
Danielle Lee and
Sharon I-Han Hsiao

2. Overview
• The context
• The problem
• The goal
• The system
• The study
University of Pittsburgh - PAWS Lab 2

3. http://www.masternewmedia.org/news/2006/12/01/social_bookmarking_services_and_tools.htm
The Wisdom of Crowds

4. Social Information Access
• Social Navigation
– Social support of user browsing
• Social Recommendation (Collaborative Filtering)
– Proactive information access
• Social Search
– Social support of search
• Social Visualization
– Social support for visualization-based access to information
• Social Bookmarking
– Access to bookmarked/shared information facilitated with tags

5. Social Navigation: The Start
• Natural tendency of people to follow
each other
– Making use of direct and indirect
cues about the activities of others
– Following trails
• Footsteps in sand or snow
• Worn-out carpet
– Using dogears and annotations
– Giving direction or guidance
• Navigation driven by the actions
from one or more advice
providers

6. • The pioneer idea of
asynchronous indirect
social navigation
• Developed for
collaborating writing and
editing
• Indicated read/edited
places in a large document
Edit Wear and Read Wear (1992)

7. Social Information Access
• History-enriched environments
– Edit Wear and Read Wear (1992)
– Social navigation systems
• Footprints, Juggler, Kalas
• Collaborative filtering
– Manual push and pull
• Tapestry, LN Recommender
– Modern automatic CF recommender systemss
• Social Search
– Quest-based systems
• AntWorld
– Group-based search (i-Spy)

8. From People to Crowds
• It started with people following other people
– ReadWear, Tapestry, AntWorld
• But we need to scale these ideas up!
• Let’s move from people to faceless crowds
– Follow-the-crowd social navigation
– Collaborative filtering
– Group-based on community-based social search
11/15/2011
University of Pittsburgh - PAWS Lab 8

9. We Lost People in Crowds…
• Crowd-based approach does work, but there are
issues
• Less trust to a faceless crowd
• Less motivation to follow
• Malicious users and attacks
• Should we step back?
– Start seeing people in crowds?
11/15/2011
University of Pittsburgh - PAWS Lab 9

10. Brusilovsky, P., Chavan, G., Farzan, R., Social Adaptive Navigation
Support for Open Corpus Electronic Textbooks, AH2004
10/19
Knowledge Sea: Social Navigation

11. MovieLens: Collaborative Filtering
11

12. I-SPY: Community-Based Search

13. HOW TO IMPROVE
RECOMMENDATIONS USING
VARIOUS SOCIAL NETWORKS
Exploring Watching Networks, Group Co-members and
Research Collaborators as a source of Recommendation
Danielle Lee

14. Why to Use Online Social Networks?
• Connection in social networks are typically
known to users
• Connected people have reasonably similar
interests
• People tend to trust their connections more than
faceless peers
• People are easily get influenced by those they
know
• Address “Cold Start” problem
• Decrease the risk of misuse and attacks

15. Paper Domain (Dataset)
Trust-based
Networks
Avesani, et al (2005) Ski Resorts (Moleskiing.it)
Al-Sharawneh & Williams (2010) General Items (Epinions)
Jamali & Ester (2009) General Items (Epinions)
Jamali & Ester (2011) General Items (Epinions) & Movies (Flixster)
Ma, et al. (2008) General Items (Epinions)
Massa & Avesani (2007) General Items (Epinions)
Walter, et al. (2009) General Items (Epinions)
DuBois, et al. (2009) Movies (FilmTrust)
Golbeck & Hendler (2006) Movies (FilmTrust)
Matsuo & Yamamoto (2007) Cosmetics (@cosme)
Friendships Bonhard, et al. (2007) Movies (MovieMatch)
Bourke, et al. (2011) Movies/TV(Facebook)
Groh & Ehmig (2007) Local Clubs (A German Site)
Liu and Lee (2010) Online Products (Cyworld)
Pera & Ng (2011) Book (Amazon and LibraryThing)
Sinha & Swearingen (2001)
Books (Amazon, Sleeper & RatingZones) and
Movies (Amazon, Reel.com, and MovieCritics)
Konstas, et al. (2009) Music (Last.fm)
Colleagues Guy, et al. (2009) Bookmarks of Web Pages (Lotus Connections)
Group Member Yuan, et al., (2009) Music (Last.fm)

16. Recommendations Based on Watching
• User-assigned unilateral connections based on their interests
– Highly object-centered relations and low personal familiarity
– Users concentrate on the usefulness of watched partners’ information
collections.
– Meets the ‘Similarity Attraction theory’ and holds ‘transitive power’.
– Mimics the process of bookmarking interesting items.
• E.g. “following” on Twitter, “plus one” on Google, “watching” on
Citeulike, “network” on Delicious and “contacts” on Flickr.
• This study is based on a Citeulike Data set provided by the system
– 97,712 Users, 3,297,156 articles, 3,869,993 bookmarks and 44,847 watching
relations
– The data set contains publications, the metadata (titles, author names,
publication name, publication years, etc.), tags and users’ bookmarks

17. Homophily in Watching Networks
• Users in watching relations have more common
information items, metadata & tags than random
pairs
– The similarity was the largest for direct connections and decreased
with the increase of social distance between users.
– In particular, users connected by watching relations tend to co-
bookmark the same items.
– The items shared by two users in direct watching relations are
more rare and have similar contents and context.
Co-
bookmarks
Jaccard Popularity
Log-
Likelihood
Title
Vector
Author
Name
Vector
Tag Vector
Direct 1.80 0.21% 8.69 .204 .1440 .0149 .0505
1 Hop .39 0.04% 7.75 .097 .0814 .0033 .0168
2 Hops .16 0.02% 7.38 .061 .0626 .0020 .0114
No
Relation
.04 0.02% 6.92 .023 .0147 .0007 .0020

18. Recommendations in Watching Networks
• Fusing watching relations with traditional collaborative
filtering recommendations improves the quality

19. Group-Based Link Homophily
• A group of people who are interested in the same topic places
uses in a specific kind of social relationship that can be used
for improving recommendations
• The homophily study based on a Citeulike Data set provided
by the system:
– 12,944 Users, 4,109 Groups and 18,793 Membership
• Information overlap between group co-members is
significantly larger than the overlap between random pairs.
Co-
bookmark
s
Jaccard Popularity
Log-
Likelihoo
d
Title
Vector
Author
Name
Vector
Tag
Vector
Group
Co-
Member
s
.26 1.01% 8.00 .050 .1117 .0222 .0595
No
Relation
.04 0.02% 6.92 .023 .0147 .0007 .0020

20. Group-based Recommendations
• Matrix Factorization Recommendations based on Group library and
Group Co-members’ library performed the best
CF – Collaborative Filtering; Gmem – Group Comembers-based; Group – Comembers & Group-based
.000
.005
.010
.015
.020
.025
.030
.035
.040
.045
.050
CF
Gmem
Group
CF
Gmem
Group
CF
Gmem
Group
CF
Gmem
Group
Jaccard Similarity Matrix
Factorization
Jaccard Similarity Matrix
Factorization
Top5 Top2_F1

21. Group-based Recommendations for
everyone?
• The idea of group-based recommendations is to pick candidate items from
those that are not yet discovered by target users, but available in the group
library and the co-members’ repositories.
• Therefore, users in the area A might not benefit from group-based
recommendation.

22. Group-based Recommendations
• Different Performance of Group-based recommendations depending
to Users’ position.
– For the dictators who dominated their group activities, the recommendations
based on group information didn’t perform well, compared with other user
clusters.
.000
.005
.010
.015
.020
.025
.030
.035
Top5_F1measure
CF
Gmem
Group
CF_SVD
Gmem_SVD
Group_SVD
.00
.01
.02
.03
.04
.05
.06
.07
.08
Top2_F1measure
CF
Gmem
Group
CF_SVD
Gmem_SVD
Group_SVD

23. Recommendations Based on Research
Collaborators
• Users in research collaborations interact to each other
personally and their relations are centered on their
research topics and the relevant by-products.
– Online social networks for professionals is to implement offline
referral chains on the Web.
• This study is based on Conference Navigator (current
version 3; hence it is CN3, now), a social adaptive system
to support conference attendees.
– 464 users, 1000 conference talks of 15 conferences, 189
collaboration relations, 144 social connections on CN3, and
5,094 bookmarks
– Data set contains conference talks, the metadata (titles, author
names, publication name, publication years, etc.), users’
bookmarks and users’ own publication records.

24. Recommendations Based on Research
Collaborators: Results
• Social Network-based Recommendations utilizing
content information of objects were the most effective
recommendation approach.
.00
.05
.10
.15
.20
.25
.30
CF
Community
CFCW
Profile
SVD
SN_Colleagues
SN_CN3
SN_Both
SN_SVD
SNCW_Colleagues
SNCW_CN3
SNCW_Both
Baseline SN SNCW
Top5_F1Measure
.00
.05
.10
.15
.20
.25
.30
.35
.40
.45
CF
Community
CFCW
Profile
SVD
SN_Colleagues
SN_CN3
SN_Both
SN_SVD
SNCW_Colleagues
SNCW_CN3
SNCW_Both
Baseline SN SNCW
Top2_F1Measure

25. References
• Watching Relation-based Recommendations
– Lee, D. H. & Brusilovsky, P. (2011) Improving Recommendations using Watching Networks in a
Social Tagging System, Proceedings of iConference 2011, Seattle, WA, USA, February 8 ~ 11, 2011
– Lee, D. H. & Brusilovsky, P. (2010) Social Networks and Interest Similarity: The Case of
CiteULike, Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (Hypertext),
Toronto Canada, June 14 ~ 16, 2010
• Group-based Recommendations
– Lee, D. H. & Brusilovsky, P. (2010) Using Self-Defined Group Activities for Improving
Recommendations in Collaborative Tagging Systems, Proceedings of the 3rd ACM Conference on
Recommender Systems (Recsys), Barcelona, Spain, September 26 ~ 30, 2010
– Lee, D. H., Brusilovsky, P. & Schleyer, T. (Under Review) Group-based Recommendations for
Individual Members, Proceedings of the 21st ACM International Conference on Information and
Knowledge Management (CIKM 2012), Maui, Hawaii,USA, October 29-November 2, 2012
• Collaborator-based Recommendations
– Lee, D. H. & Brusilovsky, P. (Under Review) Exploring Social Approach to Recommend Talks at
Research Conferences, Proceedings of the 8th IEEE International Conference on Collaborative
Computing: Networking Applications and Worksharing (CollaborateCom 2012)

26. HOW TO PROVIDE SOCIAL
GUIDANCE TO LEARNING
RESOURCES
Who guides us better – a crowd or peers?
Sharon I-Han Hsiao

27. A Quest to Building a Social QuizGuide
11/15/2011
University of Pittsburgh - PAWS Lab 27

28. 28
Good personalized guidance: improved problem solving success!
The more the students compared to their peers, the higher post-quiz scores they
received (r= 0.34 p=0.004)
Parallel Introspective Views

29. 29
• Pros: Liked OUM, interactivity with the content, social guidance
• Cons: dense and complicated with increasing activities
QuizMap

30. 30
Progressor:
• Higher Engagement: Increased the questions attempts and topic coverage
• Increased problem solving success
• Significant positive correlations between the frequencies of peer model sorting and
question attempts and success rate, r= 0.75, p< .01; r= 0.76, p< .01.
Progressor

31. The Effect of Visible Peers
QuizJET w/ IV Progressor
Parameters n=18 n=30
Peers 6.83±2.25 !"#!$%"&%'
Topics 4.00±0.79 ("))$%"&('
Questions 4.67±1.36 *"&&$%"%+'
,-./' 01'
2.-3.455'
&6'
,-./'01'7894' +:'
,-./'01'-247' 6&'
'

32. • Adding additional collection did not sacrifice the usage
• Increased the engagement (Quiz =: 5 hours, Example: 5 hours 20 mins)
• Increased diversity helped increase problem solving success
• Mix collections resulted in uniform performance
Progressor+
80.81
125.5
205.73 190.42
0
50
100
150
200
250
300
Non-adaptive Non-social ANS Progressor Progressor+
Attempts

33. References
• Hsiao, I-H. and Brusilovsky, P. (2012) Motivational Social Visualizations for
Personalized E-learning, In: Proceedings of 7th European Conference on Technology
Enhanced Education (ECTEL), ECTEL 2012, Saarbrücken, Germany, September 18-21,
2012, Springer-Verlag, (to be appeared)
• Hsiao, I-H., Guerra, J., Parra, D., Bakalov, F., König-Ries, B., and Brusilovsky, P. (2012)
Comparative Social Visualization for Personalized E-Learning. International
Working Conference Advanced Visual Interfaces, AVI 2012, Capri, Italy, May 21-25, 2012,
Proceeding AVI '12 Proceedings of the International Working Conference on Advanced
Visual Interfaces, Pages: 303-307, ACM New York, NY, USA
• Bakalov, F., Hsiao, I-H., Brusilovsky, P., and König-Ries, B. (2011) Progressor:
Personalized visual access to programming problems, IEEE Symposium on Visual
Languages and Human-Centric Computing, September 18-22, 2011, Pittsburgh, PA, USA
• Hsiao, I-H., Bakalov, F., Brusilovsky, P., and König-Ries, B. (2011) Open Social Student
Modeling: Visualizing Student Models with Parallel IntrospectiveViews.
Proceedings of 19th International Conference on User Modeling, Adaptation, and
Personalization (UMAP 2011), Girona, Spain, July 11-15, 2011, Springer, pp.171-182

34. Eventur.us
University of Pittsburgh - PAWS Lab 34

35. CoMeT (http://halley.exp.sis.pitt.edu/comet/)
University of Pittsburgh - PAWS Lab 35

36. Conference Navigator III
University of Pittsburgh - PAWS Lab 36
http://halley.exp.sis.pitt.edu/cn3/legacy.php?conferenceID=85