The Power of Known Peers: A Study in Two Domains

Peter Brusilovsky with

Danielle Lee and

Sharon I-Han Hsiao

Overview

•  The context

•  The problem

•  The goal

•  The system

•  The study

University of Pittsburgh - PAWS Lab 2

http://www.masternewmedia.org/news/2006/12/01/social_bookmarking_services_and_tools.htm

The Wisdom of Crowds

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

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”

• 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)

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)

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

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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?

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Brusilovsky, P., Chavan, G., Farzan, R., Social Adaptive Navigation Support for Open Corpus Electronic Textbooks, AH2004

10/19

Knowledge Sea: Social Navigation

MovieLens: Collaborative Filtering

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I-SPY: Community-Based Search

HOW TO IMPROVE RECOMMENDATIONS USING VARIOUS SOCIAL NETWORKS

Exploring Watching Networks, Group Co-members and Research Collaborators as a source of Recommendation

Danielle Lee

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

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)

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

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

Recommendations in Watching Networks

•  Fusing watching relations with traditional collaborative filtering recommendations improves the quality

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

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Author Name Vector

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Group Co-

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.26 1.01% 8.00 .050 .1117 .0222 .0595

No Relation .04 0.02% 6.92 .023 .0147 .0007 .0020

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

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Jaccard Similarity Matrix Factorization

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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.

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.

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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.

Recommendations Based on Research Collaborators: Results

•  Social Network-based Recommendations utilizing content information of objects were the most effective recommendation approach.

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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)

HOW TO PROVIDE SOCIAL GUIDANCE TO LEARNING RESOURCES

Who guides us better – a crowd or peers?

Sharon I-Han Hsiao

A Quest to Building a Social QuizGuide

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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

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

QuizMap

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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

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 *"&&$%"%+'

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•  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+

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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

Eventur.us

University of Pittsburgh - PAWS Lab 34

CoMeT (http://halley.exp.sis.pitt.edu/comet/)

University of Pittsburgh - PAWS Lab 35

Conference Navigator III

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